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Software Engineer Intern, AI and DL Kernel Librari...
NVIDIA is looking for outstanding Software Engineer Interns to help develop groundbreaking technologies for AI and deep learning kernel libraries. Our team builds core software that accelerates high-impact AI workloads on NVIDIA GPUs, with a strong focus on deep learning primitives, kernel libraries, and performance-critical GPU software. As an intern on the team, you will contribute to the design, development, optimization, and delivery of software that powers NVIDIA's AI platform. This internship is centered on foundational library engineering, with opportunities to work on low-level kernels, performance primitives, and efficient implementations for modern AI and deep learning workloads. You may contribute to GPU-accelerated deep learning primitives, attention kernel implementations, runtime components, code generation systems, and other performance-critical infrastructure for large language models and advanced AI applications. You will collaborate with world-class engineers across deep learning software, compilers, GPU architecture, and open-source inference ecosystems, and your work can directly impact the performance of real-world workloads at scale. What you'll be doing Contribute to production-quality software that ships as part of NVIDIA's AI software stack, including cuDNN, FlashInfer, and optimized support for large language model inference workloads. Help develop new AI systems technologies for efficient inference, with a focus on performance, scalability, maintainability, and usability. Support the design, implementation, and optimization of kernels for high-impact AI workloads across LLM inference, generative AI, computer vision, autonomous driving, and recommender systems. Assist in building extensible software abstractions for deep learning libraries, LLM serving engines, and runtime systems. Contribute to just-in-time compilation, code generation, and runtime technologies for performance-critical GPU workloads. Analyze workload performance, tune current software, and help propose improvements to future software and hardware-software interfaces. Collaborate closely with engineers across deep learning frameworks, libraries, kernels, compilers, and GPU architecture teams at NVIDIA. Contribute to open-source communities and ecosystem integrations where relevant, including projects such as FlashInfer, vLLM, and SGLang. What we need to see Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science, Electrical Engineering, or a related field. Coursework, research, or hands-on project experience in machine learning, deep learning systems, compilers, systems software, or GPU programming. Strong programming skills in C/C++ and Python. Familiarity with CUDA development and GPU programming fundamentals. Experience developing with or using deep learning frameworks such as PyTorch, JAX, TensorFlow, or ONNX. Understanding of linear algebra, performance analysis, profiling, and code optimization. Interest in software abstractions, APIs, and higher-level system architecture for performance-sensitive systems. Interest in modern machine learning and inference system trends, especially around LLMs and generative AI. Strong problem-solving skills, curiosity, and the ability to work effectively in a collaborative environment. Ways to stand out from the crowd Hands-on experience with inference engines and runtimes such as vLLM, SGLang, MLC, TensorRT-LLM, or similar systems. Background in domain-specific compilers, code generation, or library solutions for LLM inference and training. Exposure to machine learning compilers or IR systems such as MLIR, Apache TVM, TensorIR, or related technologies. Practical experience with GPU performance modeling, computer architecture, or accelerator-oriented software design. Open-source project ownership or meaningful contributions in deep learning systems, compilers, kernels, or inference infrastructure.
Senior Software Engineer, CUDA Deep Learning Syste...
We are looking for an experienced and highly motivated software professional to work on pioneering initiatives and projects at the intersection of CUDA and Deep Learning Systems. As the complexity and scale of artificial intelligence continue to grow, the intersection of advanced deep learning architectures, massive-scale distributed computing, and low-level hardware optimization has never been more critical. Our team is dedicated to exploring and prototyping next-generation ideas that bridge the gap between deep learning algorithms and CUDA, pushing the boundaries of what is possible on modern accelerator architectures. Join our dynamic, research-oriented team to help unlock maximum hardware performance for emerging AI workloads. You will be a crucial member of a highly technical group exploring uncharted territories in model optimization, custom kernel development, and cluster-scale AI systems design. If you are passionate about the fundamentals of deep learning and thrive on squeezing every ounce of performance out of advanced computing systems from a single GPU to supercomputer clusters, we want you on our team! What you will be doing: Explore, research, and prototype novel systems optimizations for advanced deep learning models at the intersection of high-level DL frameworks and low-level CUDA through modeling, simulation, and silicon prototyping. Architect and optimize distributed computing systems that scale seamlessly from a single node to massive, cluster-scale supercomputing environments. Design, implement, and optimize custom high-performance CUDA kernels tailored to emerging neural network architectures and workloads. Analyze complex hardware-software interactions to identify and resolve performance bottlenecks in both training and inference pipelines. Collaborate closely with AI researchers, HW and SW architects, kernel and compiler authors and CUDA driver experts to co-design systems and algorithms that improve accelerator compute utilization, memory bandwidth, cross-node network communication efficiency and programmability. Develop exploratory tools and runtime systems to profile and accelerate new paradigms in deep learning. Write clean, effective, and maintainable code, ensuring exploratory prototypes can smoothly transition into open-source releases, upstream framework integrations, internal tools, or closed-source commercial products. What we need to see: BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience). 8+ years of relevant industry experience or equivalent academic experience after degree achievement. Strong proficiency in C++ and Python programming. Solid background in the fundamentals of Deep Learning with a focus on transformers. Strong understanding of distributed computing principles, multi-node scaling, and the unique performance challenges of cluster-scale execution. Proven experience in systems programming, computer architecture, and low-level systems performance optimization. Familiarity with deep learning accelerator architectures such as the GPU and hands-on experience with CUDA programming, kernel optimization, and workload profiling Experience profiling and optimizing generative AI models, including but not limited to, pioneering large language models. Research background in machine learning systems or adjacent fields and experience profiling and optimizing innovative vision models, generative AI architectures, or diffusion models. A track-record of initiative and willingness to deep-dive on problems across the stack. Ways to stand out from the crowd: Deep expertise in performance internals and execution graphs of major deep learning training and inference frameworks (e.g., PyTorch, JAX, TensorRT, vLLM, sgLang, Nemo, Megatron). Hands-on experience with communication libraries (e.g., NCCL, MPI, UCX) and distributed machine learning techniques (e.g., pipeline, tensor, expert parallelism). Knowledge of numerical methods and low-precision arithmetic (e.g., NVFP4, MXFP4, FP8, INT8) and their impact on deep learning accuracy and performance. Background in deep learning compilers and ML systems, including graph-level and codegen tools (e.g., Triton, XLA, torch.compile) and highly parallel/RL-style simulation environments. Experience designing and implementing agentic AI systems applied to complex systems and infrastructure problems. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 4, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Senior Applied Research Scientist, Data Curation
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. NVIDIA’s Curator team is seeking a Senior Applied Research Scientist with experience researching, developing, and deploying deep learning models at scale across a range of modalities. You’ll join a team of Applied Research Scientists, Machine Learning and MLOps Engineers working on the next generation of data curation and document extraction pipelines for foundation-model training, including capabilities that power NVIDIA Nemotron Parse, with a focus on structured content extraction, data quality and deduplication at the petabyte-scale. At NVIDIA we’re building the foundations upon which modern LLMs are trained. Our work is a critical component in the training of Nemotron LLM models which lead the field of open LLMs. Come be a part of our world-class team building the future of Curation and Retrieval. What you’ll be doing: Working with our team of researchers to develop efficient and performant models and data pipelines that extract and curate multi-modal data (documents, image, audio and videos) used in the training of foundation models. Building pipelines for petabyte-scale extraction and content deduplication, including document and html parsing, fuzzy and near-duplicate deduplication, semantic deduplication, and substring deduplication. Contributing to the expansion and optimization of curation methodologies targeting petabyte-scale multimodal data run across hundred-node GPU clusters to improve the quality of foundation model training sets. Exploring and crafting datasets, metrics, experiments, and validation scripts to develop standard methodologies for research. These methodologies will offer customers clear guidance on which models and pipelines to apply in specific contexts. Helping ML Engineers scale pipelines to production capability through the development of NVIDIA Inference Microservices (NIMs) and blueprints which demonstrate how to deploy NIMs in a pipeline effectively. Writing papers, blog posts, documentation and training materials that help customers understand and take advantage of our research. Keeping up to date with the latest developments in data curation across academia and industry. What we need to see: Candidates with a Master's, Ph.D. or equivalent experience in data curation, document AI, information retrieval or multimodal research, along with a track record of publication in leading conferences like CVPR, ICCV, ECCV, KDD, etc. Hands-on experience developing computer vision and document-extraction models and pipelines, including layout analysis, OCR, and table, figure, or formula extraction. Kaggle Grandmaster status or a strong record of top-tier results in machine learning competitions is a strong plus. An understanding of the state of the art in data curation research, with a focus on multimodal content extraction and deduplication. 10+ years of experience developing multimodal systems across a range of models and platforms. Information retrieval experience is a big plus. Proven expertise managing distributed data frameworks like Ray, Spark, or Dask, coupled with a history of deploying massive, multi-node machine learning or data processing tasks within production environments. Knowledge of best practices in batching, streaming, and scaling of ingestion pipelines to support real-world applications. Excellent Python programming skills and a strong hands-on experience with PyTorch or comparable modern deep learning frameworks. An ability to share and communicate your ideas clearly through blog posts, papers, kernels, GitHub, etc. Excellent communication and interpersonal skills are required, along with the ability to work in a dynamic, user-focused, distributed team. A history of mentoring junior engineers and interns is a plus. Location is flexible and the team is remotely situated, focusing on NA/EU time zones. We are looking for candidates in any country where NVIDIA has an office; remote work is accepted. Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/ Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 31, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Principal Research Scientist, Synthetic Data Gener...
NVIDIA is at the forefront of the AI revolution, and our research is shaping the future of large language models. We are looking for a Principal Scientist to set the technical direction for synthetic data generation across NVIDIA's frontier model efforts. You will define and build open-source libraries within the NVIDIA NeMo ecosystem that generate synthetic datasets across text, code, structured, and multimodal data, feeding the pre- and post-training of LLMs such as Nemotron. This role combines hands-on software engineering with applied research in generative methods, and you will collaborate with research, engineering, product, and model teams as well as external labs. What you'll be doing: Build and scale data generation pipelines using LLM-based methods combined with automated quality evaluation. resulting in datasets to improve both initial training and fine-tuning of LLMs such as Nemotron. These data pipelines cover reasoning, coding, structured output, and multimodal understanding. Pioneer data generation for agentic and tool-use training: synthetic trajectories, multi-turn interactions, function calling, and executable environments for reinforcement learning, including reward modeling and verifiable-reward data Advance multimodal synthetic data generation — image, document, video, and audio — in partnership with NVIDIA's model teams. Advance privacy-preserving and safe synthesis — differential privacy, anonymization, and de-identification — enabling model training on sensitive data in regulated domains. Develop and maintain open-source libraries and SDKs with clean APIs and strong documentation. Drive software excellence with modern tooling, architecture based on configuration, and professional Git/CI-CD. Publish original research at top machine learning and AI conferences to maintain NVIDIA's technical leadership. Mentor scientists and engineers across the team, raising the technical bar and growing the next generation of researchers. What we need to see: PhD in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience. 15+ years of engineering and research experience in synthetic data generation, generative modeling, multimodal machine learning, or related areas. Deep technical understanding of LLMs, how data shapes their pre-training, post-training, and RL stages, and inference frameworks such as vLLM or TGI. Proven track record of developing or maintaining software libraries used by a broad developer community. Experience building and optimizing scalable data pipelines for large-scale model training — throughput, distributed inference, and cost at cluster scale. Strong publication record at premier venues such as NeurIPS, ICML, ICLR, ACL or similar. Ways to stand out from the crowd: Significant open-source contributions in ML or data tooling, with community adoption. Experience with multimodal generation or understanding (vision-language, document AI, video, or audio). Experience generating data for agentic, tool-use, or reinforcement-learning post-training, including RL environment design. Background in differential privacy, de-identification, or synthetic data for regulated industries such as healthcare, finance, or government. Experience influencing model training decisions at frontier scale, or partnering directly with pre-training and post-training teams. NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and talented people in the world working with us. If you are creative, autonomous, and passionate about building open-source tools that make AI safer and more private, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 8, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Staff/Lead Machine Learning Engineer, Behavior & P...
Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role We are looking for a Senior/Staff Machine Learning Engineer to be a technical leader on Nuro’s Behavior & Planning team. Our team owns how the Nuro Driver behaves on the road: prediction, decision making, and planning, and is responsible for turning Nuro’s large-scale driving data into safe, comfortable, and natural driving behavior. In this role you will bring strong, general machine learning expertise to some of the hardest problems in autonomy and drive them from research through to deployment on real vehicles. You’ll work at the frontier of applied ML spanning areas such as foundation and world models, LLM/VLM reasoning, reinforcement and imitation learning, generative and diffusion models, and transformer-based prediction and planning. You’ll apply this knowledge and experience to make our driving generalize as we scale across new geographies as we expand throughout the U.S. and globally, and across new vehicle platforms including robotaxi, personally owned vehicles, and delivery/logistics. You will help set technical direction, lead initiatives that span multiple teams, and operate with autonomy. This role will report to a Technical Lead Manager in Behavior. If you love solving hard new problems and seeing your models drive real robots in the physical world, come join us! About the Work - Technical Leadership: Lead ML initiatives across the autonomy stack framing ambiguous problems, setting technical direction, and driving them from idea to on-road deployment. - Applied ML: Design, train, and productionize state-of-the-art models across some or many of: foundation and world models, LLM/VLM reasoning, reinforcement and imitation learning, generative and diffusion models, and transformer-based prediction and planning. - Generalization & Scale: Build behavior and planning models that generalize to new cities and geographies (U.S. and global) and adapt to new vehicle platforms. - Cross-Functional Collaboration: Partner closely with Perception, Simulation & Evaluation, and ML Infra & Data, as well as the broader Autonomy and Research orgs, to develop holistic solutions to top autonomy challenges, align on priorities, and unblock shared initiatives. - Mentorship & Influence: Raise the technical bar of the team, mentor engineers and researchers, and help shape roadmaps and technical strategy beyond your immediate scope. About You Required Qualifications - 7+ years building and deploying machine learning systems, with a track record of leading complex, multi-team technical initiatives. - M.S. or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Robotics, or a related field or equivalent practical experience. - Strong, general machine learning foundations. You can reason from first principles across model architectures, training, and evaluation, and clearly explain and apply state-of-the-art techniques. Depth in some of: sequential decision making, prediction, generative modeling, foundation/world models, or representation learning. - Understanding of the full ML development cycle, from data collection and training to deployment, onboard inference considerations, and data iteration loops. - Strong problem-solving and programming skills in Python (required) and/or C++. - Demonstrated ability to lead initiatives, collaborate across many different teams, and operate independently with minimal oversight. - Efficient and clear communication skills are a must, with the ability to align cross-functional stakeholders. Preferred Qualifications - Strong preference for prior experience in autonomous vehicles or robotics, and familiarity with how ML fits into a complete autonomy/planning stack, though we hire strong general-ML talent from adjacent domains and value the ability to ramp on a new domain quickly. - Experience mentoring engineers or leading a team. Interest in growing into a management or tech-lead track is not required but those paths may exist for strong performers. - Familiarity with at least one major ML framework (PyTorch, JAX, TensorFlow). - Research contributions in top venues (e.g., NeurIPS, ICLR, ICML, CVPR, RSS, CoRL) are a plus, but a track record of applied and production impact matters more. At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $235,030 and $352,290 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package. At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics.
ML Research Scientist, Prediction & Smart Agents
Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role The mandate of the prediction team is to use advanced machine learning techniques to improve the behavior of the Nuro Driver. As a key member of the Prediction and Smart Agents team, you will focus on building state-of-the-art models for predicting the behavior of surrounding traffic. These models are crucial for our autonomous system, as they will be deployed onboard as part of our planning stack and used offboard for realistic closed-loop simulation. You will explore novel machine learning methods to solve challenging real-world problems in autonomous driving. This work includes using generative sequence modeling approaches for robustly predicting complex, interactive traffic situations. It requires deep reasoning about the intentions of other road users and how their behaviors influence safe and correct driving decisions. You will also use different input modalities, including End-to-End (E2E) approaches, for predicting other agents. A vital component of this role is building smart, controllable agents to enable effective closed-loop training in simulation. If you are passionate about solving challenging new problems, leading impactful research, and seeing your work deployed onto real robots, we encourage you to apply! About the Work - Design and build scalable, machine learning-based prediction systems to generate multi-modal, realistic, and kinematically feasible trajectories. - Conduct cutting-edge research in generative sequence modeling and sequential decision-making. Areas of interest include, but are not limited to: - Scalable generative sequence modeling approaches. - Marginal, conditional, and joint distribution modeling for interactive agents. - Transformer-based encoder-decoder architectures. - Large generative models and diffusion models. - Controllability of agents via conditioning, guidance, and other techniques. - Collaborate closely with the Planning team to design realistic and controllable agents for closed-loop simulation, enabling agent training via Reinforcement Learning (RL). - Mitigate accumulated uncertainties across interconnected autonomy components. - Collaborate across various autonomy teams to develop holistic solutions for top challenges, proposing ideas, prioritizing. - Derive practical, deployable solutions and see them deployed on real-world vehicles About You You have deep expertise and prior experience in some or many of the following areas: - Education: You have an M.Sc. or Ph.D. (preferable) focusing on one or more of the following areas: Computer Science, Artificial Intelligence, Mathematics, or a closely related field - Expertise: Subject matter expertise and research experience in one or more of the following: sequential decision-making, prediction, Imitation Learning, Deep Reinforcement Learning, generative modeling, large models (pretraining/finetuning), or machine learning for robotics. - Technical Skills: You have strong problem solving and programming skills in Python (required) and C++ (beneficial) and ML frameworks such as PyTorch. - Collaboration: Strong culture fit and good team player. - Experience: You have 2+ years of deploying machine learning systems onboard, ideally in the area of prediction. - Publications: Demonstrated research publications in top conferences (e.g. NeurIPS, ICLR, ICML, CVPR, RSS, CoRL, ICRA, IROS etc.) Nice to have: Deep background in Embodied AI for robotics, Causal reasoning, Model interpretability and explainability, Joint prediction and planning, Understanding of Diffusion Models. At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $193,930 and $291,150 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package. At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #LI-DNP
Research Engineer, Agentic Systems
Mirage is an AI video company focused on making creation dramatically easier. Our team tackles some of the hardest creative and technical challenges in generative media. We get to rethink how people collaborate with AI that can actually do creative work with them—defining entirely new interaction paradigms for AI-native video tools. We make that possible by building multimodal agents that turn creative direction into edits, rendering engines that composite multiple layers of video and graphics, and generative video models trained from the ground up that create new media when needed. All of this comes together in Captions, our creative workspace for generating, editing, and designing video. The technology behind Captions is now available more broadly: Tesseract gives any AI agent native video-editing capabilities, while developers can integrate our models into their own products through APIs. Explore our work Captions https://apps.apple.com/us/app/captions-ai-edits-your-video/id1541407007 — Our flagship creative workspace Tesseract https://x.com/trymirage/status/2102429594804429138 — Our professional video engine for AI agents Research https://mirage.app/research?utm_source=chatgpt.com — The foundation models we build in-house Updates https://x.com/trymirage — The latest from Mirage TechCrunch https://techcrunch.com/2026/03/24/mirage-raises-75m-to-continue-building-models-for-its-ai-video-editing-app-captions/, Forbes https://www.forbes.com/companies/captions/?list=ai50, Fast Company https://www.fastcompany.com/91270234/video-most-innovative-companies-fast-company-2025-youtube-roku-tubi-vimeo-captions-descript-cour-procreate-synthesia-beeble — Press Our Investors We’re very fortunate to have some the best investors and entrepreneurs backing us, including Index Ventures, Kleiner Perkins, Sequoia Capital, Andreessen Horowitz, General Catalyst, Uncommon Projects, Kevin Systrom, Mike Krieger, Lenny Rachitsky, Antoine Martin, Julie Zhuo, Ben Rubin, Jaren Glover, SVAngel, 20VC, Ludlow Ventures, Chapter One, and more. Please note that all of our roles will require you to be in-person at our NYC HQ (located in Union Square) About the Role Mirage is seeking an ML Engineer to push the boundaries of large language models for multimodal creative tasks. You'll develop new approaches for building and extending agentic systems that understand and operate over complex, real-world data, particularly video. This role focuses on advancing agent capabilities, improving reasoning and control, and enabling new forms of interaction between language models and time-based media. Responsibilities - Design and build end-to-end agentic systems for creative tasks - Develop novel approaches for training and adapting the large language models that power these agents - Design new objectives, datasets, and fine-tuning strategies to improve agent behavior and reliability - Explore multimodal reasoning and structured generation for creative control - Run systematic experiments to evaluate and improve agent performance in real-world tasks - Design evaluation frameworks for agentic workflows in video analysis and editing - Analyze failure modes across the full agent loop (planning, tool use, execution) and iterate on improvements What makes you a great fit - BS/MS/PhD in CS, ML, or related field - Strong track record building production ML systems or agentic pipelines - Deep understanding of transformers and modern LLM techniques - Experience with fine-tuning, alignment, or post-training methods, especially for adapting models to generate structured outputs or drive tool use - Comfort owning the full stack, from model-level experiments to deployed agent systems - Strong experimental rigor and good taste for what makes agents actually work in practice BENEFITS: - Comprehensive medical, dental, and vision plans - 401K with employer match - Commuter Benefits - Catered lunch multiple days per week - Dinner stipend every night if you're working late and want a bite! - Grubhub subscription - Health & Wellness Perks - Multiple team offsites per year with team events every month - Generous PTO policy Captions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please note benefits apply to full time employees only.
Senior / Staff Machine Learning Engineer, Applied...
Your Impact at LILA We are growing our Applied AI org and seeking talented Senior/Staff Machine Learning Engineers with expertise in LLM training, evaluation, and production-oriented ML systems. You’ll work on improving Lila’s AI models for customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated, and used in real customer contexts. This is a rare chance to join an early team with the autonomy, flexibility, and compute to tackle frontier science problems. Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through experimentation, and ensure model behavior works well end to end inside the application. This role is ideal for someone who can bridge research and engineering: training or adapting models, building evaluation loops, debugging model behavior, and collaborating across AI and Software to move promising capabilities into production-quality systems. What You'll Be Building - Close the last-mile gap between Lila AI model capabilities and customer-specific scientific workflows. - Build evaluation loops that measure model quality, reliability, and customer fit. - Design experiments to improve model performance across applied customer use cases. - Feed customer learnings, data signals, and evaluation results back into the Lila AI model improvement cycles. - Partner with AI researchers to translate model improvements into usable capabilities. - Work with Software to integrate model behavior into end-to-end product workflows. - Debug model failures using traces, evaluations, customer context, and scientific feedback. - Build reusable tooling for model adaptation, evaluation, and deployment workflows. What You'll Need to Succeed - Strong experience building, training, adapting, or evaluating machine learning models. - Strong software engineering skills in Python and modern ML frameworks such as PyTorch, JAX, or TensorFlow. - Experience with distributed ML training frameworks (Megatron-LM, TorchTitan, DeepSpeed, Ray) - Experience designing experiments, evaluation metrics, or test sets for model performance. - Ability to debug model behavior using data, traces, logs, and qualitative feedback. - Experience working across research and engineering teams to move ML capabilities into usable systems. - Familiarity with large language models, multi-modal models, or agentic AI systems. - Clear communication skills for translating customer needs into technical model improvements. Bonus Points For - Experience adapting models for customer-facing or production workflows. - Experience with scientific, technical, or data-intensive customer use cases. - Experience building evaluation harnesses, model monitoring, or quality dashboards. - Familiarity with retrieval-augmented generation, tool use, or agentic workflows. - Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL. - Experience training MoE architectures. - Experience working with product or customer-facing teams to translate needs into ML improvements. Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program. International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market. Expected Base Salary Range $180,000 - $298,000 USD About LILA Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy . A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
Senior / Principal ML Scientist, Foundation Models...
Your Impact at LILA Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), Machine Learning engineers build and operate the systems that turn foundation models over biological sequence, molecular structure, and experimental data into production capabilities powering automated scientific discovery across Lila's life science domains. We are seeking a Principal ML Engineer to design, build, and scale the ML infrastructure behind those models. Much of the team's current work is in structure prediction and co-folding for antibody and biologics design, alongside sequence design and multimodal scientific reasoning. You will own critical systems end to end, from training pipelines and distributed compute to model deployment and integration into Lila's closed-loop discovery engine. This is a high-impact IC role for someone who operates at the intersection of ML systems engineering and life science applications. You will shape the technical direction for how ML models are trained, evaluated, and deployed at scale, collaborate closely with AI scientists and experimental researchers to close the computational–experimental loop, and drive Lila's ML infrastructure toward the next generation of capabilities. What You'll Be Building - Design, build, and optimize large-scale training pipelines for structure prediction, co-folding, and other generative models on biological and chemical data, including distributed training across GPU clusters - Own production ML systems end to end: model deployment, serving infrastructure, monitoring, and reliability for models used in Lila's scientific workflows - Architect ML infrastructure that supports rapid iteration across structure prediction, sequence design, and multimodal scientific reasoning workloads - Make structure prediction and co-folding models fast and cheap enough to run at campaign scale, where inference volume is often the bottleneck on scientific throughput - Drive the engineering side of Lila's "Lab-in-the-Loop" lifecycle: build pipeline models, integrate experimental feedback loops, and ensure model outputs are actionable for downstream scientific workflows - Define and advance ML engineering standards, tooling, and best practices across the AI organization - Collaborate with AI scientists to translate research prototypes into robust, scalable production systems, bridging the research-to-deployment gap What You’ll Need to Succeed - Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience) - Extensive hands-on experience building and operating production ML systems at scale - Deep expertise in distributed training infrastructure, including experience with large-scale GPU clusters (AWS, GCP, or on-prem) - Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices - Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) with experience optimizing training and inference performance - Demonstrated ability to drive technical direction for ML infrastructure independently, from architecture through implementation - Track record of cross-functional collaboration with research scientists, translating between ML methodology and engineering execution Bonus Points For - Experience building training or inference infrastructure for generative models applied to biological sequences, molecular structures, or scientific data - Experience supporting structure prediction or co-folding workloads, including AlphaFold-derived methods (e.g., Boltz, Protenix), diffusion models, or protein language models - Experience with agentic frameworks, active learning loops, or closed-loop experimental workflows - Contributions to open-source ML tools, frameworks, or infrastructure projects - Familiarity with at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or nucleic acid design) - Experience with model evaluation frameworks for scientific applications where ground truth is sparse or delayed Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program. International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market. Expected Base Salary Range $268,000 - $384,000 USD About LILA Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy . A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
Senior Application Security Engineer, AI and Machi...
Who We Are Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction. Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in. We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute. The Way We Work The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice: - Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping. - Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through. - Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together. - Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work. - Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most. - Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact. What We're Looking For We are looking for a Senior Application Security Engineer to help secure our AI, machine learning, and inference platforms. This is a hands on technical role focused on building security into modern AI infrastructure, inference systems, and developer platforms. You will work closely with platform engineers, ML engineers, and infrastructure teams to identify risks, design secure architectures, and build security tooling that enables engineers to move quickly and safely. This role is execution focused. You will drive technical implementation, perform deep security reviews, and help build out our application security capabilities alongside the CISO and engineering leadership. This role is hybrid out of our Seattle or San Francisco offices, with an in-office requirement of at least 2 days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this role at this time. What You’ll Do Secure AI and Machine Learning Systems - Perform threat modeling across AI platforms, inference services, and ML pipelines - Identify risks such as prompt injection, model extraction, adversarial inputs, and data leakage - Review model serving architectures and inference pipelines - Partner with ML engineers to secure training, fine tuning, and deployment workflows - Help design isolation and security controls for multi tenant AI workloads Application Security Engineering - Perform architecture and design security reviews - Conduct targeted code reviews for high risk components - Identify security gaps in APIs, micro-services, and distributed systems - Build secure patterns for authentication, authorization, and service to service communication - Help engineering teams implement secure defaults and guardrails Inference Platform Security - Secure customer facing inference APIs and services - Protect against abuse, model extraction, and adversarial behavior - Design rate limiting, isolation, and workload protection controls - Build monitoring and detection for anomalous inference behavior AI Supply Chain and Model Security - Evaluate open source models and dependencies - Secure model artifacts and distribution pipelines - Implement integrity validation and provenance controls - Help secure container images and runtime environments Security Automation and Tooling - Build security automation for AI and application pipelines - Integrate security scanning into CI/CD workflows - Develop tooling to help engineers detect and fix issues early - Improve developer experience with security guardrails What You'll Need Required Experience - Strong background in application security engineering - Experience performing threat modeling and architecture reviews - Experience securing APIs and distributed systems - Experience working in cloud environments such as AWS, GCP, or Azure - Experience with containers and Kubernetes - Strong scripting or programming skills such as Python, Go, or similar - Experience working closely with engineering teams to implement security improvements AI and Machine Learning Experience - Experience securing ML pipelines, inference systems, or data platforms - Familiarity with risks such as prompt injection, model extraction, and adversarial inputs - Experience reviewing model serving architectures - Understanding of training data security and data leakage risks It's a Strong Plus If You Have - Red team or offensive security experience - Experience crafting payloads and evaluating CVEs for exploitability in diverse environments - Experience with GPU infrastructure or high performance computing - Experience with Hugging Face, PyTorch, TensorFlow, or similar frameworks - Experience with LLM systems, RAG pipelines, or agent frameworks - Experience building security automation pipelines - Experience securing multi tenant infrastructure What Success Looks Like - Security is embedded into AI platform architecture early - Engineering teams ship quickly with secure defaults - Inference platforms are resilient against abuse and extraction - Model pipelines are secure and auditable - Security tooling scales with engineering growth Why This Role Matters This role sits at the intersection of application security, AI infrastructure, and developer platforms. You will help define how we secure modern AI systems while enabling engineers to move quickly and safely. You will work closely with engineering leadership and the CISO to build practical, scalable security capabilities that support rapid innovation in AI and machine learning. Compensation We are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, our total rewards package for eligible roles includes a discretionary bonus, a meaningful equity component, and comprehensive benefits. The anticipated annual base salary range for this role is: $180,000 - $220,000 USD Benefits and Perks We offer a comprehensive and competitive benefits package designed to support our employees’ health, well-being, and long-term success: - Comprehensive Health Coverage: Medical, dental, and vision coverage for employees and eligible dependents. - Meaningful Equity: RSUs that give employees a stake in the company's long-term success. - Retirement Savings: 401(k) matching (U.S.) and pension contributions (U.K.). - Flexible Time Off: Unlimited PTO, company holidays, and floating holidays to support work-life balance. - Company-Wide Winter Break: Two weeks of company closure each winter to disconnect and recharge. - Paid Parental & Family Leave: Paid leave to support you and your family through life's important moments. - Professional Development: Annual learning and development allowance to support your professional growth. - Wellness Benefits: Wellness and work-from-home stipends to support your physical and mental well-being. - Sabbatical Program: Four weeks of paid sabbatical leave after four years of service. - Flexible Work: Flexible schedules and a hybrid work model for our office-based teams. - In-Office Meals: Complimentary meals at our office hubs. Benefits may vary by location, team, and role. At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.
ML Research Engineer, London
Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease. The future is coming. A future enabled and enriched by the incredible power of machine learning. A future in which diseases are curtailed or cured starting with better and faster drug discovery. Come and be part of an interdisciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring and collaborative culture. The world we want tomorrow is the one we’re building today. It starts with the culture at this company. It starts with you. About Iso Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed. Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases. We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design. Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI. Research Engineering (Machine Learning), London We are looking for Research Engineers with different levels of experience - Mid through to Senior, Staff, Principal or equivalent levels. Your impact This is an exciting opportunity for you to contribute to frontier research at the intersection of AI and drug design. Working in a highly creative, iterative environment, you will be partnering with scientists and engineers to advance foundational models that will transform the biopharmaceutical world as we know it. You will draw upon your existing engineering and Machine Learning experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems. What you will do Implementation & Optimisation: - Translate research concepts into practical implementations by developing and optimising state-of-the-art AI models, and building and maintaining robust codebases, data pipelines, and infrastructure for training and evaluation. Experimentation & Evaluation: - Design, implement, and run experiments to evaluate the performance and robustness of ML models, using a full spectrum of state-of-the-art machine learning methods. Evaluating, tuning, and maintaining AI/ML models (which includes collecting and preparing data as needed) Evaluation & Inference: - Implement algorithms and software to analyse and evaluate the performance of AI models. - Optimising performance of AI/ML models such as Diffusion models, Transformers, GNNs, leveraging a deep understanding of the AI/ML hardware+software stack - Advise on how to bring AI/ML models to production and/or integrating them into product offerings, and monitoring and refining their behavior. - Developing specialised tools/frameworks/infrastructure to aid in the work above Collaboration & Knowledge Sharing: - Work closely with research scientists and engineers, contributing to team discussions, sharing knowledge, and actively participating in code reviews to foster a collaborative environment. Innovation & Impact: - Proactively identify and address technical challenges, stay updated on the latest AI advancements, and focus on developing solutions that enable scaling our wider foundation and applied model platforms. - Ability to execute on independent engineering projects and software development towards research goals. Skills and Qualifications Essential - Academic Background: Advanced degree (Master’s or PhD) in a highly quantitative field (Computer Science, AI, Physics, Mathematics, etc.) or equivalent practical experience. - ML Fundamentals: Deep understanding of machine learning principles and techniques. - Framework Expertise: Strong proficiency in deep learning frameworks such as JAX or PyTorch. - Modern Architectures: Hands-on experience building and working with modern model architectures (e.g., Transformers, GNNs, Diffusion Models). - Full ML Lifecycle: Experience taking models from conception to production (scoping, data analysis, training, debugging, evaluation, benchmarking, and deployment). - Engineering Excellence: Excellent software development skills with strong algorithms and data structures fundamentals. - Collaboration & Communication: An excellent team player with strong written and verbal communication skills, able to collaborate seamlessly in a cross-disciplinary environment. - Agency: Self-directed with an ability to navigate ambiguity, propose and own complex projects, learn the necessary context, and readily adapt to new domains and developments. Nice to have - Proven Research Record: A history of scientific contributions (e.g., publications at NeurIPS, ICML, ICLR, CVPR) or significant contributions to state-of-the-art AI models. - Scale & Performance: Experience training models across distributed systems (multi-GPU/multi-node) and optimising training and inference performance (e.g., XLA, Triton, CUDA, Pallas). - Domain Knowledge: A strong interest in, or knowledge of, biochemistry, computational biology, or drug discovery fundamentals. - Industry Experience: Proven track record working in reputable tech companies or research labs. - Applied ML: Experience developing models developed for real-world applications. - Infrastructure: Solid technical infrastructure knowledge and experience with low-level engineering (e.g., GCP, Kubernetes, Docker) Culture and values We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. Thoughtful Thoughtful at Iso is about curiosity, creativity and care. It is about good people doing good, rigorous and future-making science every single day. Brave Brave at Iso is about fearlessness, but it’s also about initiative and integrity. The scale of the challenge demands nothing less. Determined Determined at Iso is the way we pursue our goal. It’s a confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we. Together Together at Iso is about connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere. Creating an extraordinary company We believe that to be successful we need a team with a range of skills and talents. We're building an environment where collaboration is fundamental, learning is shared and every employee feels supported and able to thrive. We value unique experiences, knowledge, backgrounds, and perspectives, and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. Hybrid working It’s hugely important for us to share knowledge and build strong relationships with each other, and we find it easier to do this if we spend time together in person. This is why we follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on which team you’re in). If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for an initial screening call. Please note that when you submit an application, your data will be processed in line with our privacy policy . >> Click to view other open roles at Isomorphic Labs
Senior/Staff AI Research Engineer
Hume AI is seeking talented researchers and engineers interested in working with our AI research team to build state-of-the-art speech-language models (SLMs). Our new SLM training method—reinforcement learning from human expression (RLHE)—learns human preferences from behavior in millions of audio and video recordings, making SLMs superhumanly helpful, interesting, funny, eloquent, honest, and altruistic. Join us in the heart of New York City and contribute to our endeavor to ensure that AI is guided by human values, the most pivotal challenge (and opportunity) of the 21st century. About Us Hume AI is a Series B startup dedicated to building artificial intelligence that is directly optimized for human well-being. As the first company to release speech language models, we’re focused on expanding our research to encompass audio understanding models and evaluation platforms for enterprises. Our goal is to enable a future in which technology draws on an understanding of human emotional expression to better serve human goals. As part of our mission, we also conduct groundbreaking scientific research, publish in leading scientific journals like Nature , and support a non-profit, The Hume Initiative, that has released the first concrete ethical guidelines for empathic AI ( www.thehumeinitiative.org ). You can learn more about us on our website ( https://hume.ai/ ) and read about us in WIRED , Forbes , and Venturebeat . About the Role We are looking for talented researchers and engineers interested in working with our team to build state-of-the-art audio models and evaluation tools. In this role, you will build out software systems that support distributed model training, inference, and benchmarking as well as massive-scale data collection, storage, preprocessing, and analysis. You will be working to solve some of today’s most exciting AI research problems at industry scale. Requirements - Expertise in the Python ecosystem and popular ML libraries and tools (e.g. PyTorch) - Experience writing robust and maintainable production-ready code - Comfort iterating quickly on new and uncertain research directions - 2+ years of experience training and/or fine-tuning transformer models with large-scale datasets of text, audio, image, and/or video data Application Note Please apply only to the position that best aligns with your qualifications. If you submit multiple applications or have applied within the past 6 months, only your initial submission will be considered. Annual Salary $180,000 - $350,000 USD
Applied Scientist, Reinforcement Learning (Mid, Se...
ROLE MISSION As HAI's LLM Post-Training Applied Scientist, you will own the reinforcement learning and on-policy distillation pipeline that transforms raw model capability into reliable, safe clinical behavior. Your post-training methods will directly determine how our AI agents reason through complex clinical scenarios, handle safety-critical decisions, and ultimately impact millions of patient interactions. This role exists because post-training is where capability becomes trustworthiness—and in healthcare, that's everything. WHAT YOU WILL ACCOMPLISH Own your first major outcome: By day 90, you will have shipped a post-training improvement that meaningfully advances model performance on a critical clinical capability (clinical reasoning, safety alignment, or task completion), evaluated the gains rigorously, and contributed that learning to our post-training roadmap. Drive lasting impact: At 12 months, you will have designed and shipped multiple post-training methods that measurably improve our models' clinical safety and reasoning, built reusable infrastructure (reward models, verifiers, evaluation frameworks) that accelerate future post-training work, published your research or contributed to HAI's intellectual property, and directly shaped how our deployed models behave in production healthcare environments. THE TEAM You'll work alongside ML researchers, engineers, clinicians, and safety experts who are obsessed with building trustworthy AI. This is a highly technical team that values rigor, collaboration across disciplines, and solving the hardest problems in AI safety and alignment. You'll have direct influence on model architecture and training decisions. WHAT YOU'LL DO - Design and implement RL and OPD post-training methods including RLHF, RLVR, on-policy distillation, and novel approaches tailored to healthcare AI—selecting the right methods for different clinical reasoning and safety challenges - Build and evaluate reward models, verifiers, and LLM-as-judge pipelines that provide reliable training signals for post-training, ensuring they capture what truly matters in clinical contexts (accuracy, safety, patient experience) - Develop conversational AI environments and simulations for healthcare RL training—creating synthetic clinical scenarios and datasets that enable safe, scalable post-training without relying solely on human feedback - Automate post-training research loops using agents and tooling to systematically explore hyperparameters, methods, and data strategies—turning post-training into a scientific, reproducible process - Run rigorous experiments and analysis to understand what drives post-training gains, isolate the contributions of different components, and build intuition about what works in healthcare contexts - Collaborate with research, engineering, and clinical teams to translate clinical requirements into post-training objectives, validate improvements against real-world metrics, and scale successful methods to production LOCATION REQUIREMENT We believe the best ideas happen together. To support fast collaboration and a strong team culture, this role is expected to be in our Menlo Park office five days a week, unless otherwise specified. Required Qualifications - Master's degree in Computer Science, Machine Learning, or a related field - 5+ years of professional experience in NLP, LLM training, or reinforcement learning - 2+ years of hands-on experience with RL for LLM post-training - Proficiency in Python and PyTorch for large-scale training - Demonstrated experience with RLHF, RLVR, LLM-as-judge, or similar post-training methods - Experience training or fine-tuning models at scale (50B+ parameters) Preferred Qualifications - Publications at top-tier ML venues (NeurIPS, ICML, ICLR, ACL, EMNLP) - Healthcare or regulated domain experience - Experience with distributed training frameworks (FSDP, DeepSpeed, vLLM) - Familiarity with safety alignment and interpretability research Our comprehensive compensation package is designed to reward your expertise and includes both a competitive base salary and valuable stock options. Individual offers are determined based on a variety of factors, including your professional experience, core competencies, and geographic location. WHY JOIN HIPPOCRATIC AI Reinvent healthcare with AI that puts safety first. We’re building the world’s first healthcare‑only, safety‑focused LLM — a breakthrough platform designed to transform patient outcomes at a global scale. This is category creation. Work with the people shaping the future. Hippocratic AI was co‑founded by CEO Munjal Shah and a team of physicians, hospital leaders, AI pioneers, and researchers from institutions like El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft, and NVIDIA. Backed by the world’s leading healthcare and AI investors. We recently raised a $126M Series C at a $3.5B valuation, led by Avenir Growth, bringing total funding to $404M with participation from CapitalG, General Catalyst, a16z, Kleiner Perkins, Premji Invest, UHS, Cincinnati Children’s, WellSpan Health, John Doerr, Rick Klausner, and others. Build alongside the best in healthcare and AI. Join experts who’ve spent their careers improving care, advancing science, and building world‑changing technologies — ensuring our platform is powerful, trusted, and truly transformative. EQUAL OPPORTUNITY Hippocratic AI is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, sex, age, disability, sexual orientation, gender identity or expression, genetic information, military or veteran status, or any other characteristic protected by applicable law. We are committed to building a team that reflects the patients we serve. We actively encourage applications from candidates of all backgrounds. If you require accommodations during the hiring process, please contact people@hippocraticai.com. Please be aware of recruitment scams impersonating Hippocratic AI. All recruiting communication will come from @hippocraticai.com http://hippocraticai.com email addresses. We will never request payment or sensitive personal information during the hiring process.
Machine Learning Engineer, Detection and Tracking
Who we are Helsing develops artificial intelligence-enabled capabilities to protect and defend democracies. We build Altra, an AI-powered drone software platform, and HX-2, our autonomous drone. We are growing our US operations, cultivating an ambitious and committed team of mission-driven professionals to apply their skills to solve challenging problems. The role You will own the detection and tracking models that power Helsing's products — training, tuning, and deploying models against US-specific datasets. This is an applied ML role: you won't be writing research papers, but you will be expected to have strong intuition for model performance, data quality, and the practical trade-offs involved in getting detection and tracking systems to work reliably in production. You will manage the full model lifecycle — from assessing and curating training data through annotation, training, evaluation, and deployment to edge platforms. The day-to-day - Training and fine-tuning detection models (YOLO, DETR, Faster R-CNN, and similar architectures) on mission-specific datasets - Implementing and improving multi-object tracking pipelines (SORT, DeepSORT, ByteTrack, or similar) - Evaluating model performance: analyzing metrics, diagnosing failure modes, and iterating on data and model improvements - Managing the data pipeline end-to-end: assessing raw data, coordinating annotation, curating datasets, and implementing augmentation strategies - Optimizing models for deployment on SWaP-constrained and embedded platforms (quantization, pruning, TensorRT, ONNX export) - Collaborating with systems engineers to integrate models into the broader Altra platform - Working across sensor modalities as needed, including electro-optical, infrared, and other imaging sources You should apply if you - Have 5+ years of experience in applied machine learning or computer vision - Have a Bachelor's degree in Computer Science, Electrical Engineering, or a related field; Master's or PhD strongly preferred - Have production experience training and deploying object detection models — not just research or academic projects - Are proficient in Python and PyTorch or a comparable deep learning framework - Have strong intuition for data quality; you can look at annotated datasets, training curves, and evaluation metrics and know what's wrong - Have experience with the full model training lifecycle: data curation, annotation management, training, evaluation, and deployment - Have experience optimizing models for deployment on SWaP-constrained and edge platforms (TensorRT, ONNX, quantization) - Understand multi-object tracking and have implemented or worked with tracking algorithms in practice - Can read and contextualize scientific papers in computer vision and apply findings to production systems - U.S. citizenship required Nice to have - Strong proficiency in Rust or C++ for production model deployment and optimization - Experience with multiple sensor modalities — particularly infrared or thermal imaging - Familiarity with MLOps tooling: experiment tracking (MLflow, Weights & Biases), dataset versioning, model registries - Experience with annotation tools and workflows (CVAT, Label Studio, or similar) - Background in computer vision beyond detection — segmentation, pose estimation, activity recognition - Experience with simulators, emulators, or synthetic data generation for training and evaluation - Experience deploying models on GPU-accelerated embedded platforms (NVIDIA Jetson, similar) - Background in defense, intelligence, or other mission-critical environments Join Helsing and work with world-leading experts in their fields - Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns - The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world - Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defense industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward - In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts - We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practice responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about What we offer - A focus on outcomes, not time-tracking - A generous compensation and benefits package (in addition to base salary) that includes, but may not be limited to, insurance coverage (medical and travel), flexible paid time off, paid holidays, and remote and/or hybrid work available depending on position. All compensation and benefits are subject to the terms and conditions of the underlying plans or programs, as applicable and as may be amended, terminated or superseded from time to time. Helsing is an Equal Opportunity Employer. We will consider all qualified applicants without regard to race, color, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, genetics, or any other characteristic protected by applicable federal, state, or local law. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation. Helsing's Candidate Privacy and Confidentiality Regime can be found here .
Research Scientist (post-training)
We are Genmo, a research lab developing the world’s most sophisticated video world models to understand, simulate, and interact with the physical world. Our mission is to unlock the right brain of AGI. Join us in advancing physical intelligence and enabling robots to learn and act in a changing world. ROLE OVERVIEW: We are seeking an exceptional Research Scientist to join our team, focusing on alignment and post-training techniques for large-scale video generation models. In this role, you will be at the forefront of ensuring our diffusion-based video models reliably produce high-quality, physically accurate and safe outputs that match human preferences and values. KEY RESPONSIBILITIES: - Lead research initiatives in alignment and post-training methods for video generation models, focusing on improved quality, reliability, and adherence to human intent - Design and implement supervised fine-tuning and reinforcement learning from human feedback (RLHF) pipelines for video generation models - Develop robust evaluation frameworks to measure model alignment, safety, and output quality - Create and optimize data collection pipelines for human feedback and preferences - Design and conduct experiments to validate alignment techniques and their scaling properties - Collaborate with cross-functional teams to integrate alignment improvements into our production pipeline - Stay at the cutting edge of the field by regularly reviewing academic literature in both generative AI and alignment - Mentor junior researchers and foster a culture of responsible AI development - Work closely with product teams to ensure alignment methods enhance rather than inhibit model capabilities QUALIFICATIONS: - Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field - Must have: - Strong publication record in top-tier conferences (e.g., NeurIPS, ICML, ICLR) with a focus on reinforcement learning, alignment, or generative models - Extensive experience implementing and optimizing large-scale training pipelines using PyTorch - Deep understanding of reinforcement learning techniques, particularly RLHF - Experience with distributed training systems and large-scale experiments - Proven track record in designing and implementing robust evaluation frameworks - Excellent communication skills with the ability to explain complex technical concepts to diverse audiences - Strong software engineering skills and experience with complex shared codebases - Ideal candidate will have: - Experience with diffusion models or other generative architectures - Background in fine-tuning large language models or generative models - Experience working with human feedback data collection and annotation pipelines - Strong aesthetic sense and understanding of video quality assessment - Familiarity with alignment techniques such as constitutional AI or debate - Track record of successful collaboration with product teams - Experience with perceptual quality metrics and human evaluation design - Contributions to open-source projects in AI alignment or generative AI Additional Information The role is based in the Bay Area (San Francisco). Candidates are expected to be located near the Bay Area or open to relocation. Genmo is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law. Genmo, Inc. is an E-Verify company and you may review the Notice of E-Verify Participation https://drive.google.com/file/d/1urlF3XKQTw83aJfpMKMwrUP1E1mUlNTO/view?usp=sharing and the Right to Work posters in English and Spanish https://drive.google.com/file/d/1ZD4zax6Li8oTN_VHd9w3rpFPoWeaeGVO/view?usp=drive_link.
Research Scientist: Post-Training
ABOUT GENERALIST At Generalist, we are on a mission to build general intelligence for the physical world and make it useful to everyone. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done. We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world. The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google DeepMind, and other frontier labs—with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models and vision-language-action models (PaLM-E, https://research.google/blog/palm-e-an-embodied-multimodal-language-model/ RT-2 https://deepmind.google/blog/rt-2-new-model-translates-vision-and-language-into-action/, Gemini Robotics https://deepmind.google/models/gemini-robotics/), launched and scaled ChatGPT https://chatgpt.com/ and GPT-4 https://openai.com/index/gpt-4-research/ to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas https://bostondynamics.com/atlas/, Spot https://bostondynamics.com/products/spot/, Stretch https://bostondynamics.com/products/stretch/) and pushed the limits of what they can do (from parkour https://www.youtube.com/watch?v=tF4DML7FIWk to manipulation https://bostondynamics.com/blog/large-behavior-models-atlas-find-new-footing/, and testing robustness https://www.youtube.com/watch?v=aFuA50H9uek). We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. ABOUT THE ROLE Pretraining gives us a general model. Post-training makes it useful, controllable, safe, and performant in the real world. You will train large pretrained robot models into production-ready systems via fine-tuning, reinforcement learning, steering, human feedback, task specialization, evaluation, and on-robot validation—at scale. Regardless of your initial background, you will grow into becoming a full-stack ML roboticist capable of quickly pinpoint issues on either side of ML or controls, and all the places in between. This is where research meets reality. You’ll be responsible for: - Designing fine-tuning and adaptation strategies for downstream robotic tasks and embodiments - Developing methods for improving reliability, robustness, and controllability - Building evaluation frameworks that measure real-world robot performance, not just offline metrics - Improving inference-time performance (latency, stability, memory footprint) in collaboration with ML infrastructure - Leveraging techniques such as imitation learning, RL, distillation, synthetic data, and curriculum learning - Closing the loop between model outputs and physical-world outcomes You might thrive in this role if you: - Have experience with fine-tuning large models for downstream tasks (RLHF, IL, RL, distillation, domain adaptation, etc.) - Have worked on embodied AI, robotics, or real-world ML systems - Care deeply about evaluation, benchmarking, and failure analysis - Are comfortable debugging across the ML stack — from loss curves to robot behavior - Enjoy rapid iteration with real-world feedback loops - Want to bridge the gap between foundation models and physical deployment
Research Scientist
Dyna Robotics builds general-purpose robots powered by a proprietary embodied AI foundation model with top-in-industry generalization and real-world performance. Already deployed with customers across multiple industries, our robots do commercial-grade work in the physical world. Our team comes from Google DeepMind, Meta, and Cruise, and we're backed by CRV, First Round, and other leading investors. THE ROLE As a Research Scientist on the AI Research team, you will own the end-to-end loop of Embodied AI. You will leverage our massive, "in-the-wild" multimodal datasets to develop models that don’t just move - they generalize, self-improve, and redefine what is possible in unstructured human environments. WHAT YOU’LL DO - Advance the Frontier: Design and implement state-of-the-art robot learning algorithms (RL, Imitation Learning, Diffusion) for complex, high-DOF manipulation tasks. - VLA & Video Generation: Architect and scale multi-modal foundation models (VLMs/VLAs) and leverage generative video architectures to enhance world-modeling and robot foresight. - End-to-End Ownership: Own the full research-to-production pipeline: from task definition and data curation to large-scale training and real-time deployment on physical hardware. - Physical-World Evaluation: Move beyond the benchmark. You will evaluate and iterate on models using our expanding robot fleet, ensuring commercial-grade performance in dynamic customer environments. - Collaborative Leadership: Work at the intersection of AI, Hardware, and Teleop teams to enhance the "Intelligence-per-Watt" of our robotic systems. WHAT YOU’LL BRING - Research Excellence: A PhD (preferred) or Master’s in CS/Robotics with a significant publication record in top-tier conferences (RSS, CoRL, ICRA, NeurIPS, CVPR, or ICLR). - 5+ Years of Mastery: Deep experience in AI and robotics research, specifically in deploying deep learning models onto physical robots (not just simulation). - Generative AI Expertise: Hands-on experience with Vision-Language-Action (VLA) models, Video Generation, or large-scale transformer architectures. - Technical Stack: Mastery of Python and PyTorch (or JAX). Familiarity with simulation stacks like MuJoCo, Isaac, or Drake. - High-Quality Code: The ability to write maintainable, production-ready research code that scales across GPU clusters. BONUS POINTS FOR - Experience with founding-stage startups or leading small, elite research teams. - Deep understanding of classical robotics (controls, state estimation) to complement learning-based approaches. - Experience profiling and optimizing foundation models for low-latency inference on edge devices. At Dyna Robotics, we build technology for the real world, which requires a team as diverse as the environments our robots inhabit. We are an equal opportunity employer committed to technical rigor and mutual respect. Don’t let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you’re passionate about robotics, no matter your discipline, we want to hear from you, even if you don't check every box.
Sr. Staff ML Researcher, LLM Algorithmic Optimizat...
At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration. We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? Together, we can help shape the endless possibilities of AI. Role Overview d-Matrix is seeking a Senior Staff ML Researcher to join our Algorithms team and develop new methods for running large language models efficiently on our DNN accelerators. This is a hands-on research role at the intersection of machine learning, applied mathematics, numerical computing, and AI hardware. You will invent, evaluate, and implement algorithmic techniques that improve LLM inference performance while preserving model quality, with a focus on challenges such as numerical precision, model compression, memory efficiency, and efficient execution on accelerator hardware. You will work closely with mathematicians, ML researchers, ML engineers, compiler engineers, and hardware architects to translate research ideas into practical solutions for cutting-edge generative AI workloads. What You Will Do - Research and develop algorithmic and numerical techniques that improve the latency, throughput, memory efficiency, and accuracy of large language model inference on d-Matrix DNN accelerators. - Design and evaluate methods such as low-precision quantization, model compression, sparsity, pruning, distillation, low-rank approximation, and other techniques for efficient neural-network execution. - Optimize key transformer inference workloads, including matrix operations, attention mechanisms, and KV-cache utilization, based on model characteristics and hardware constraints. - Analyze tradeoffs among model quality, numerical precision, memory footprint, compute efficiency, and system performance. - Build high-quality research prototypes and evaluation frameworks in Python to test new ideas across relevant models, datasets, and workloads. - Partner with hardware, compiler, and software teams to map algorithms effectively to accelerator capabilities, including supported data types, memory hierarchy, data movement, and compute architecture. - Stay current with advances in efficient LLM inference, neural-network optimization, numerical methods, and hardware-aware machine learning, and apply relevant developments to d-Matrix products. What You Will Bring - Master's degree or PhD in Computer Science, Mathematics, Statistics, Physics, Electrical Engineering, or a related quantitative field, with 5+ years of relevant hands-on experience. - Strong foundation in machine learning and applied mathematics, with the ability to apply mathematical or numerical methods to practical ML problems. - Hands-on experience developing and evaluating machine-learning algorithms, models, or research prototypes. - Strong Python programming skills and experience with object-oriented code design. - Experience with modern machine-learning frameworks such as PyTorch, JAX, or TensorFlow. - Experience improving the performance, efficiency, or quality of machine-learning models through algorithmic or numerical techniques. - Familiarity with neural-network architectures; experience with transformers, LLM inference, or hardware-aware ML optimization is advantageous but not required. - Ability to design rigorous experiments, analyze results, and communicate technical findings clearly to cross-functional partners. Equal Opportunity Employment Policy d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day. d-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.
Principal System Software Engineer, AI Inference E...
At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration. We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? Together, we can help shape the endless possibilities of AI. Location: Hybrid, working onsite at our Santa Clara, CA, headquarters 3 days per week. The role: Principal System Software Engineer, AI Inference Execution What you will do: The role requires you to be part of the team that helps productize the SW stack for our AI compute engine. As part of the software team, you will be responsible for the development, enhancement, and maintenance of the next-generation AI deployment software. You have had past experience working across all aspects of the full-stack toolchain and understand the nuances of what it takes to optimize and trade-off various aspects of hardware-software co-design. You are able to build and scale software deliverables in a tight development window. You will work with a team of system software experts to build out the deployment infrastructure, working closely with other software (ML and compilers) and hardware experts in the company. What you will bring: Minimum: - BS in Computer Science, Engineering, Math, Physics, or related degree with 12+ years of industry software development experience and MS in Computer Science, Engineering, Math, Physics, or related degree preferred with 6+ years - Strong grasp of system software, data structures, computer architecture, and machine learning fundamentals - Proficient in C/C++/Python development in Linux environment and using standard development tools - Experience with distributed, high-performance software design and implementation - Self-motivated team player with a strong sense of ownership and leadership. Preferred: - MS or PhD in Computer Science, Electrical Engineering, or related fields - Experience with inference servers/model serving frameworks (such as TensorRT-LLM, vLLM, SGLang, etc.) - Experience with deep learning frameworks (such as PyTorch and TensorFlow) - Experience with deep learning runtimes (such as ONNX Runtime, TensorRT, etc.). - Experience with distributed systems collectives such as NCCL and OpenMPI - Experience with software testing fundamentals - Experience deploying ML workloads (LLMs, VLMs, NLP, etc.) on distributed systems. - Experience with Kubernetes, Ray, or other MLOps tools and techniques used from definition to deployment - Prior startup, small team, or incubation experience - Work experience at a cloud provider or AI compute/subsystem company Equal Opportunity Employment Policy d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day. d-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.
Full Stack Software Engineer
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. ABOUT THE TEAM Anduril Labs is a multidisciplinary team of technologists, solution architects, engineers, and military subject matter experts. Its mission is to drive innovation across all Anduril business units and product lines by developing novel concepts and next-generation technologies that enhance Anduril’s competitive edge and give its customers a decisive technological advantage over its adversaries. ABOUT THE JOB We are looking for a talented full stack software engineer to join our growing team at Anduril Labs in Washington, DC. As a full stack software engineer in Anduril Labs, you will help bring innovative, next-generation concepts to life through proof-of-concept development and rapid prototyping using bleeding edge technologies. The ideal candidate has exceptional software development and creative problem-solving skills, is a self-starter, and can quickly grasp complex concepts. As a full stack software engineer, you possess the skills to architect, develop, and deploy distributed applications and services, including both front-end and back-end components. You have experience with agile, end-to-end software development lifecycle and are comfortable developing and deploying code across Windows and Linux-based systems (including standalone bare-metal hardware, virtualized environments, and cloud-hosted platforms). Embedded software development experience is a plus. You are also proficient in integrating legacy code and systems, leveraging open-source technologies, and developing and utilizing APIs. Additionally, you have a solid understanding of AI/ML core concepts (e.g., feature extraction, supervised vs. unsupervised learning, regression, classification, clustering, deep learning neural networks, NLP, LLMs, SLMs, model fine-tuning, prompt engineering, RAG) and hands-on experience developing (Gen)AI-enhanced applications or services. We also expect candidates to have familiarity with database technologies (e.g., SQL, NoSQL, Graph DB, Vector DB) and experience with data modeling, data wrangling, analytics, and visualization. Since Anduril Labs supports all Anduril businesses and product lines, you will have the unique opportunity to work closely with multi-disciplinary engineering and product development teams across the entire company. This means you will get to directly contribute to the development of Anduril’s next-generation products and services. So if you thrive in a dynamic environment that values creative problem-solving, love writing code, excel as both an individual contributor and team player, are eager to learn, and bring a can-do attitude, this role is for you. WHAT YOU’LL DO - Lead the development of prototypes to demonstrate advanced concepts in areas like autonomous and multi-agent systems, GenAI, advanced data analytics, quantum computing/sensing/networking/comms/machine learning, modeling, simulation, optimization, visualization, next-gen human-machine interfaces, heterogenous computing, and cybersecurity. - Own the entire Software Development Lifecycle from inception through development, testing, deployment, and documentation for Anduril Labs-developed software prototypes. - Interface and collaborate with other Anduril and customer engineering teams, and strategic partners. - Support Anduril- and customer-funded R&D efforts. - Participate in field experiments and technology demonstrations. REQUIRED QUALIFICATIONS - 3+ years of programming with Python, C++, Java, Rust, Go, or JavaScript/TypeScript. - Proven software architecture and design skills. - Ability to quickly understand and navigate complex systems and established codebases. - AI/ML development using commercial and open-source AI frameworks, models, and tools (e.g., Jupyter Notebook, PyTorch, TensorFlow, Scikit-learn, OpenAI, Claude, Gemini, Llama, LangChain, YOLO, AWS Sagemaker, Bedrock, Azure AI, RAG). - Web app development (e.g., React, Angular, or Vue). - Cloud development (e.g., AWS, Azure, or GCP). - Data modeling and wrangling. - Networking basics (e.g., DNS, TCP/IP vs. UDP, socket communications, LDAP, Active Directory) - Database technologies (e.g., SQL, NoSQL, Graph DB, Vector DB). - API development and integration (e.g., REST, GraphQL). - Containerization technologies (e.g., Docker, Kubernetes). - Software development on Linux and Windows. - Demonstrable hands-on experience using GenAI tools (e.g., OpenAI Codex , Claude Code, Gemini Code Assist, GitHub Copilot, Amazon CodeWhisperer, or similar) for software development, code generation, debugging, and algorithmic exploration. - Experience with Git version control, build tools, and CI/CD pipelines. - Demonstrated understanding and application of software testing principles and practices, including unit testing, integration testing, and end-to-end testing. - Strong problem-solving skills, meticulous attention to detail, and the ability to work effectively in a collaborative team environment. - Excellent communication and interpersonal skills, with the ability to effectively articulate complex technical concepts to diverse audiences. - Eligible to obtain and maintain an active U.S. Top Secret SCI security clearance PREFERRED QUALIFICATIONS - BS in Computer Science, Engineering, or similar field. - Distributed applications development (e.g., client/server, microservices, multi-agent solutions). - High performance computing (HPC) and big data technologies (e.g., Apache Spark, Hadoop). - Mobile app development (e.g., iOS or Android). - Embedded software development experience. - Willingness to travel up to approximately 10% US Salary Range $132,000 - $198,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
ML Research Scientist (MLRS)
WHY ACHIRA At Achira, we are building a team of world-class scientists, ML researchers, and engineers to move beyond the beaten path and build a frontier lab for Physical AI for molecules. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed. At Achira, you’ll operate at the frontier scale of massive compute, massive data, and massive ambition. You’ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We’re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success. ABOUT THE ROLE We’re looking for deep learning researchers who want to build rich representations of atomistic systems to help teach AI about the microscopic world. You will collaborate with domain experts in molecular modeling to develop Achira’s next-generation foundation models. You’ll work at the intersection of model architecture, data, and training strategy to find new points on the Pareto frontier of representational richness, robustness, accuracy, and speed for microscale world models. While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities. WHAT YOU’LL DO - Build a robust pre-, mid-, and post-training curriculum that ensures foundation model performance and impact. - Create reinforcement learning strategies to help models focus their capacity where it matters most, especially when the training data doesn’t cover the domain of applicability. - Develop expressive representations of molecular and atomistic structure and dynamics, including equivariant graph neural networks, geometric transformers, and latent encoders that capture physical symmetries and constraints. - Prototype, benchmark, and iterate rapidly to transform research ideas into reusable and scalable components across Achira’s ecosystem. - Collaborate with physicists and chemists to ensure models are grounded in real physics. - Work with research engineers and the infrastructure team to identify where research ideas will need support in order to deliver effective results. ABOUT YOU - Drive to apply modern ML techniques to solve problems at the frontier of the microscopic world. - A willingness to follow the data and embrace an empirical approach to the bitter lesson. - A pragmatic approach to inductive bias (eg. physical priors, equivariance) in model building. - Experience designing, running and analyzing ML experiments at scale. - Experience with 3D geometric deep learning. - Demonstrated research impact through conference talks or publications (in machine learning venues), open-source contributions, or released models. - Strong interdisciplinary communication and presentation skills and the ability to translate ideas and concepts to colleagues from non-ML backgrounds. - Proficiency in Python and modern ML frameworks (PyTorch, JAX). - Experience collaborating on research projects across multi-person teams. - Desire and comfort with working on frontier problems in physical AI to invent the blueprint for how they will be tackled. NICE TO HAVE Achira values excellent ML researchers from many backgrounds, and expect members of the team to contribute complementary strengths. If the work excites you, we encourage you to apply, even if you hit none of the bonus features listed below! - Machine learning researcher with professional experience (post-degree) in an industry setting. - Experience with self-supervised representation learning techniques. - Experience with Bayesian deep learning techniques and uncertainty quantification. - Experience developing or applying generative models for 3D systems. - Experience with equivariant graph neural network architectures (NequIP, MACE, SchNet, PaiNN, or similar). - Prior experience working in or with researchers in the domains of computational chemistry, biology, or materials science. - Experience working with multi-cloud distributed compute systems. - Experience working with multi-site distributed company team.
BioMedical AI Research Engineer
About Xaira Therapeutics Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London. About the Role We are seeking a Biomedical AI Engineer to design and deploy agentic AI systems that can reason over biomedical knowledge, plan multi-step analyses, and autonomously integrate diverse scientific data sources to accelerate therapeutic discovery. This role goes beyond traditional LLM + RAG pipelines. You will build AI agents that can retrieve, synthesize, evaluate, and act on biomedical information across literature, omics datasets, and clinical records operating as intelligent systems embedded within discovery workflows. Key Responsibilities - Design and implement agentic AI architectures capable of multi-step reasoning, planning, and tool use in biomedical contexts. - Develop LLM-based agents that dynamically retrieve, rank, and synthesize knowledge from scientific literature, knowledge graphs, omics datasets, and clinical data. - Build memory and feedback mechanisms that allow agents to refine hypotheses, update context, and adapt to evolving data. - Integrate external tools (analysis pipelines, simulation engines, statistical models, databases) into autonomous agent workflows. - Architect scalable systems that combine structured and unstructured biomedical data within production-grade AI environments. - Collaborate closely with computational biologists, translational scientists, and software engineers to embed agentic systems into real-world discovery pipelines. - Evaluate robustness, reliability, and interpretability of autonomous systems in high-stakes biomedical settings. Qualifications - Degree in Computer Science, Machine Learning, Computational Biology, Biomedical Informatics, or related field. - Strong hands-on experience with large language models, including fine-tuning, alignment, and structured prompting. - Experience building agent-based systems or complex LLM orchestration frameworks (e.g., multi-agent systems, tool-using LLMs, planning modules). - Proficiency in Python and modern ML frameworks (PyTorch, JAX, TensorFlow). - Experience working with large-scale biomedical datasets (genomics, transcriptomics, clinical records, or scientific corpora). Preferred - Experience designing autonomous research assistants or AI systems that perform multi-step scientific reasoning. - Familiarity with knowledge graphs, hybrid symbolic-neural systems, or multimodal foundation models. - Understanding of privacy, security, and regulatory considerations in healthcare AI. Compensation We offer a competitive compensation and benefits package, seeking to provide an open, flexible, and friendly work environment to empower employees and provide them with a platform to develop their long-term careers. A Summary of Benefits is available for all applicants. We offer a competitive package that includes base salary, bonus, and equity. The base pay range for this position is expected to be $227,000 - $295,000 annually; however, the base pay offered may vary depending on the market, job-related knowledge, skills and capabilities, and experience. Xaira Therapeutics an equal-opportunity employer. We believe that our strength is in our differences. Our goal to build a diverse and inclusive team began on day one, and it will never end. TO ALL RECRUITMENT AGENCIES: Xaira Therapeutics does not accept agency resumes. Please do not forward resumes to our jobs alias or employees. Xaira Therapeutics is not responsible for any fees related to unsolicited resumes.
Senior Staff ML Engineer, Driver Understanding and...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgements and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo Driver. We are looking for researchers and software engineers who are passionate about developing machine learning techniques. These techniques are for the Evaluation systems on our autonomous service. They will serve as a constant driver to improve the performance of our technology stack. You will: - Grow the end-to-end strategy for our next generation of machine learning-based evaluation metrics, promoting scientific and statistical rigor across our embodied AI applications - Architect and build scalable systems for training and fine-tuning large-scale generative models to produce realistic and evaluate interesting driving behaviors - Lead the design, implementation, and iteration of novel RL algorithms, reward functions, and training paradigms tailored for generating high-fidelity and insightful driving behaviors - Lead the development of cutting-edge Deep Learning models and Generative AI (LLM/VLM) solutions. These solutions will enhance human-led triaging, introduce automation for high-volume workflows, and perform nuanced analysis of self-driving behavior to detect critical anomalies - Proactively monitor and assimilate best practices from within Alphabet and the broader industry to develop a novel Reinforcement Learning from Human Preference (RLHF) based data collection and evaluation system - Provide technical mentorship, guidance, and thought leadership to other engineers within the team and across collaborating groups - Guide and align multiple teams—including Driver Understanding, Simulation, System Engineering, Research, and Onboard Software—on a cohesive evaluation strategy, ensuring cross-functional alignment on goals and priorities You have: - PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience - 10+ years of hands-on experience in developing and applying Machine Learning models, with a significant focus on Reinforcement Learning - 2+ years of people management experience - Demonstrated expertise in deep learning, sequence modeling, and generative models - Strong publication record or history of impactful project delivery in RL or related areas - Proficiency in Python and standard ML frameworks (e.g., JAX, TensorFlow) - Experience with large-scale distributed training and data processing - Proven ability to lead complex and ambiguous technical projects from conception to completion We prefer: - 12+ years of relevant experience in ML/RL research and application - Experience in the autonomous vehicles domain, robotics, or complex simulation environments - Deep understanding of state-of-the-art RL techniques, including those used for fine-tuning large models (e.g., from human feedback/preferences) - Familiarity with large-scale simulation platforms and their integration with ML training workflows - Experience designing and using metrics for evaluating complex AI systems - Track record of technical leadership, influencing senior stakeholders, and driving innovation across team boundaries - Excellent communication skills, with the ability to articulate complex technical concepts clearly The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $281,000 - $356,000 USD
Research Scientist, Map Scalability
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. You will: - Solve fundamental and applied research problems related to world understanding using VLMs, generative modeling and 3D reconstruction at large scale. - Prototype and iterate on various research ideas using Waymo's internal driving data - Scale and productionize world understanding solutions in collaboration with engineering teams across Waymo - Present research findings to a wide audience within Waymo and Alphabet, with the possibility of publishing results to the research community You have: - Master’s or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Deep Learning, or in a similar discipline. - 1-3+ years of experience in a related field. - Expert in either Foundational VLMs, Computer Vision or learning-based 3D reconstruction methods (3DGS, NeRF). - Strong ML software engineering skills in Python/C++ with an ability to rapidly prototype solutions. - Familiarity with major ML Frameworks (JAX, Tensorflow, Pytorch). We prefer: - Publications at top-tier conferences like CVPR/ICCV/ECCV/ICLR/ICML/NeurIPS/IROS/CoRL etc. - Hands-on experience productionizing and scaling ML solutions beyond the research stage. - Fundamentals in 3D vision and/or computer graphics. - Domain experience in the autonomous vehicle or simulation space. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $213,000 - $263,000 USD
2027 Summer Intern, PhD, Machine Learning, Compute...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you’re a software engineer or researcher who’s curious and passionate about Level 4 autonomous driving, we'd like to meet you. Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship! You will: - Analyze and characterize internal feature representations of deep multimodal perception foundation models. - Design and implement active learning and intelligent data curation algorithms to identify rare, safety-critical edge cases. - Explore automated data labeling and validation workflows leveraging modern Vision-Language Models (VLMs). - Train, fine-tune, and evaluate deep neural networks to improve model performance. - Validate model performance on large-scale autonomous vehicle sensor datasets and simulation environments. You have: - Currently enrolled in a PhD program in Computer Science, Robotics, Electrical Engineering, or a related quantitative field. - Experience programming in Python and C++. - Practical experience training, fine-tuning, and evaluating deep learning models for computer vision or multimodal perception (e.g., using PyTorch, JAX, or TensorFlow). - Solid understanding of modern neural architectures (e.g., Vision Transformers, multi modal sensor encoders). We prefer: - Research experience or publications in active learning, self-supervised representation learning, core-set selection, or multi modal foundation models (VLMs). - Hands-on experience working with multi-camera or 3D LiDAR perception systems in robotics or autonomous driving. - Experience building VLM evaluation/judge pipelines or large-scale data mining workflows. - Familiarity with high-dimensional feature analysis and clustering techniques. Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in. The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly PhD Pay $85 - $85 USD
Senior Scientist I, Applied Machine Learning and G...
Passionate about precision medicine and advancing the healthcare industry? Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time. The Senior Scientist, Applied Machine Learning and Generative AI, Pharma R&D will perform complex computational analyses and develop algorithms, causal models, and agent-based tools to advance the Tempus platform supporting drug R&D. The ideal candidate will possess strong applied machine learning, causal inference, and generative AI skills, with experience building and applying LLMs, agentic systems, causal frameworks, and foundation models in the life sciences. The candidate will also be proficient in communicating complex findings to various stakeholders. Description: Data Expertise: Tempus has one of the largest multimodal patient datasets ever collected, providing a unique opportunity to work with extensive and diverse data. Become an expert in Tempus’ vast epidemiological, clinical, genomic, transcriptomic and pathology imaging data, along with the latest tools and techniques for their analysis and modeling. Innovation: Drive continual improvement of the Tempus platform for pharmaceutical R&D by championing and building new machine learning and generative AI capabilities based on client needs and and industry trends. Teamwork and collaboration: Work with Research, Engineering & Data Science teams across Tempus’ expansive data science community to develop and deliver innovative computational solutions. Co-develop solutions with Pharma partner science and clinical teams Drug R&D Expertise: Work with leading pharmaceutical companies. Gain proficiency in their strategies, drug modalities, and pipelines to identify where the Tempus platform can add value. Scientific Communication: Skillfully navigate client interactions to extract and communicate the most impactful insights driving new R&D opportunities; effectively communicate complex technical results and methodologies to diverse external stakeholders. Scientific Leadership & Influence: Empower computational biologists and RWE scientists through targeted AI guidance and hands on coaching to increase AI tool adoption to maximize impact. Personal development: Continuously immerse yourself in the latest industry trends, best practices, and advancements in machine learning and AI to revolutionize drug R&D Qualifications: Education and experience: Minimum PhD (or Masters degree with 2+ years of relevant experience). Plus an additional 2+ years of relevant industry or post-doctoral experience. Combining: Quantitative and computational skills, with a focus on causal AI, causal inference, and/or explainable AI (e.g. Causal Machine Learning, Generative AI, Mathematics, biostatistics). Biological, medical, or drug development knowledge and data (e.g. oncology, RWE, medical science, or clinical drug development). Technical/Scientific Skills: Proficient in R, Python, and SQL, with specific expertise in frameworks for agentic orchestration (e.g., LangChain, LangGraph, AutoGen, or DSPy). Depp knowledge of machine learning and statistical modeling, with hands-on experience in causal methodologies (e.g., Directed Acyclic Graphs, counterfactual reasoning, and heterogeneous treatment effect estimation). Applicable knowledge of LLM-driven agent architectures, including experience with prompt engineering, RAG (Retrieval-Augmented Generation), and function calling/tool use. Awareness of the uses of machine learning in molecular/biomedical data analysis or drug discovery/development. Experience working with molecular data, clinical trial and/or real-world data and Track record of success: proven in peer reviewed publications. Communication Skills: Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences. Comfort in a client-facing role and ability to deliver technical training to both internal and external audiences. Motivated: Thrive in a fast-paced environment and willing to shift priorities seamlessly. Preferred Skillsets/Background: Experience in integrative modeling of multi-modal clinical and omics data. Strong understanding of data and artificial intelligence in drug R&D. Understanding of cancer biology. Previous experience working with large transcriptome and NGS data sets, or clinical or real-world medical data. #LI-DA1 CHI: $140,000-$200,000 NYC/SF: $150,000-$210,000 The expected salary range may vary for other locations. Actual salary may vary based on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Research Engineer, Generative Video
Mirage is an AI video company focused on making creation dramatically easier. Our team tackles some of the hardest creative and technical challenges in generative media. We get to rethink how people collaborate with AI that can actually do creative work with them—defining entirely new interaction paradigms for AI-native video tools. We make that possible by building multimodal agents that turn creative direction into edits, rendering engines that composite multiple layers of video and graphics, and generative video models trained from the ground up that create new media when needed. All of this comes together in Captions, our creative workspace for generating, editing, and designing video. The technology behind Captions is now available more broadly: Tesseract gives any AI agent native video-editing capabilities, while developers can integrate our models into their own products through APIs. Explore our work Captions https://apps.apple.com/us/app/captions-ai-edits-your-video/id1541407007 — Our flagship creative workspace Tesseract https://x.com/trymirage/status/2102429594804429138 — Our professional video engine for AI agents Research https://mirage.app/research?utm_source=chatgpt.com — The foundation models we build in-house Updates https://x.com/trymirage — The latest from Mirage TechCrunch https://techcrunch.com/2026/03/24/mirage-raises-75m-to-continue-building-models-for-its-ai-video-editing-app-captions/, Forbes https://www.forbes.com/companies/captions/?list=ai50, Fast Company https://www.fastcompany.com/91270234/video-most-innovative-companies-fast-company-2025-youtube-roku-tubi-vimeo-captions-descript-cour-procreate-synthesia-beeble — Press Our Investors We’re very fortunate to have some the best investors and entrepreneurs backing us, including Index Ventures, Kleiner Perkins, Sequoia Capital, Andreessen Horowitz, General Catalyst, Uncommon Projects, Kevin Systrom, Mike Krieger, Lenny Rachitsky, Antoine Martin, Julie Zhuo, Ben Rubin, Jaren Glover, SVAngel, 20VC, Ludlow Ventures, Chapter One, and more. Please note that all of our roles will require you to be in-person at our NYC HQ (located in Union Square) About the Role Mirage is seeking an ML Engineer to build and scale the systems powering our video generation models. You’ll work on novel modeling approaches, training objectives, scaling strategies, and inference optimization and efficiency to bring cutting-edge models into production. This role sits at the intersection of research and systems engineering, focusing on making advanced models faster, more efficient, and capable of ultra-low latency, real-time generation. Responsibilities - Train and optimize large-scale video and multimodal models - Improve efficiency across training and inference (memory, latency, cost) - Implement techniques such as distillation, quantization, and pruning to aggressively accelerate diffusion and autoregressive generation - Build and maintain distributed training systems - Optimize GPU utilization, parallelism, and throughput - Develop tooling for experimentation, evaluation, and debugging - Translate research models into robust, production-ready systems - Monitor and improve model performance in real-world usage What makes you a great fit - BS/MS/PhD in CS, ML, or related field - 2+ years of professional industry experience - Strong experience in deep learning systems and infrastructure - Expertise in PyTorch, CUDA, Triton, and distributed training (FSDP, etc.) - Experience scaling and optimizing large models under low-latency inference constraints - Strong debugging and performance profiling skills - Ability to move quickly from prototype to production BENEFITS: - Comprehensive medical, dental, and vision plans - 401K with employer match - Commuter Benefits - Catered lunch multiple days per week - Dinner stipend every night if you're working late and want a bite! - Grubhub subscription - Health & Wellness Perks - Multiple team offsites per year with team events every month - Generous PTO policy Captions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please note benefits apply to full time employees only.
Forward Deployed Research Scientist
Shape the Future of AI At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially. About Labelbox We're the only company offering three integrated solutions for frontier AI development: - Enterprise Platform & Tools : Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale - Frontier Data Labeling Service : Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models - Expert Marketplace : Connecting AI teams with highly skilled annotators and domain experts for flexible scaling Why Join Us - High-Impact Environment : We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions. - Technical Excellence : Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence. - Innovation at Speed : We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution. - Continuous Growth : Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI. - Clear Ownership : You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics. Role Overview Alignerr is Labelbox's human data organization — we produce the training data that frontier AI labs use to build their most capable models. Our Forward Deployed Research Team sits at the intersection of research science and client delivery, embedding research capability directly into the engagements that drive our business. This is not a traditional research scientist role. You will not spend months pursuing a single research question. You will work on multiple client engagements simultaneously, operating on timescales of days to weeks. You will sit in scoping meetings with research teams at major AI labs, reason scientifically about data strategy in real time, fine-tune open-weight models to validate our data methodology, and collaborate with our Applied Research team to turn client-grounded findings into published work. The pace is fast, the problems are applied, and the feedback loops are short. We are looking for someone who finds that energizing, not compromising. Your Impact - Engage directly with frontier lab research teams. You will be in the room during client scoping meetings — not as support staff, but as a technical peer. You'll engage on methodology, challenge assumptions about data requirements, and shape project specifications based on a scientific understanding of how data composition affects model outcomes. Develop deep scientific understanding of client engagements. For each project, you will build a working model of the client's architecture, training methodology, and target capabilities. You'll use this understanding to reason about why a particular data strategy will or won't work, identify risks early, and iterate with empirical grounding — not intuition. Run ablation studies and fine-tune open-weight models. You will fine-tune models on client data (and proxy data) to empirically measure the impact of our data on model performance. This is how we validate that what we deliver actually improves our customers' models — and how we catch problems before the client does. Consult on workflow and quality systems. You will partner with our Human Data Operations team to review annotation schemas, task designs, and quality rubrics before projects go into execution. Your job is to ensure the spec is technically sound — that the data we produce will actually serve the client's training objectives. Collaborate with Applied Research on publications and benchmarks. Our Applied Research team owns the long-horizon research agenda. Your role is to feed them signal from the field — generalizable findings, reusable methodologies, empirical results — and help drive joint projects to completion. You will contribute to benchmarks, white papers, and conference submissions that establish Labelbox's research credibility. What You Bring - Required - MS or PhD in Machine Learning, NLP, Computer Science, or a related quantitative field. - Hands-on experience fine-tuning large language models (open-weight models such as Llama, Mistral, Qwen, or similar). - Strong understanding of LLM training pipelines — pretraining, supervised fine-tuning, RLHF/DPO, and how data quality and composition affect each stage. - Experience designing and executing experiments with rigor — hypothesis formation, controlled comparisons, statistical analysis of results. - Ability to operate at speed. You should be comfortable going from problem definition to experimental results in days, not months. - Strong written and verbal communication. You will present findings to client research teams and contribute to published work. Strongly Preferred - Prior experience at a frontier AI lab, applied ML startup, or in a research role with direct client/stakeholder interaction. - Experience with evaluation and benchmarking of LLMs — designing metrics, building eval harnesses, interpreting results critically. - Familiarity with human data pipelines — annotation workflows, quality assurance methodology, inter-annotator agreement analysis. - Experience with reinforcement learning, reward modeling, or RLHF environments. - Published research (conferences, journals, or technical reports) in ML/NLP or adjacent fields. What Matters More Than Credentials - Applied instinct over academic purity. The measure of success here is client impact and publishable-but-practical results — not methodological novelty for its own sake. If your first instinct when handed a problem is to build a framework, this isn't the role. If your first instinct is to run an experiment and get a result, it is. - Comfort with ambiguity and incomplete information. Client engagements rarely come with clean problem statements. You'll need to extract the real question from a noisy conversation, scope an approach quickly, and iterate. - Cross-functional fluency. You will work daily with field engineers, project managers, operations teams, and an independent Applied Research team. Someone who can only operate within a pure research silo will struggle here. - Intellectual honesty. When an ablation study shows the data isn't working, you need to say so — clearly and constructively — even when it's inconvenient for the deal timeline. What You Should Know About This Team - We are small and high-leverage. The FDRT is a team of five today. Every person's work directly influences client outcomes and Labelbox's market position. - We operate at the tempo of client delivery. Two-week sprints. SLAs measured in days. If you want months of uninterrupted focus on a single problem, our Applied Research team is a better fit. - We are at the intersection of several teams. FDRT works with Field Delivery Engineers, Human Data Operations, Applied Research, and client research teams. The role requires navigating those interfaces with credibility and without ego. - We protect time for research. 25–30% of team capacity is allocated to research collaboration with Applied Research. This is not aspirational — it is a structural commitment. You will have the opportunity to publish. Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location. Annual base salary range $200,000 - $300,000 USD Life at Labelbox - Environment : Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making - Growth : Career advancement opportunities directly tied to your impact - Vision : Be part of building the foundation for humanity's most transformative technology Our Vision We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs. Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs. Your Personal Data Privacy : Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice . Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.
Senior Security Engineer (AI Safety), London, Laus...
Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease. The future is coming. A future enabled and enriched by the incredible power of machine learning. A future in which diseases are curtailed or cured starting with better and faster drug discovery. Come and be part of an interdisciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring and collaborative culture. The world we want tomorrow is the one we’re building today. It starts with the culture at this company. It starts with you. About Iso Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed. Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases. We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design. Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI. Your impact As a Senior AI Security Engineer at Isomorphic Labs, you will operate at the absolute frontier of Artificial Intelligence and Cybersecurity. Reporting directly to the CISO, your mission is to architect, secure, and future-proof our pioneering AI-first platform and autonomous agentic workflows that drive the next generation of drug discovery pipelines. In this high-impact role, you will design custom risk management frameworks tailored to the unique threat landscape of AI in life sciences, establish strict governance over critical model artifacts, and embed seamless security controls directly into distributed training and inference infrastructure. You will serve as the technical anchor bridging machine learning engineering, platform architecture, and adversarial defense. Operating in a fast-moving, high-stakes environment, you will lead AI-specific incident response and pioneer model-driven defense mechanisms to automate threat hunting and mitigation. Your work will ensure that breakthrough scientific research is paired with an uncompromising, resilient defense safeguarding invaluable intellectual property while accelerating our innovation velocity. What you will do - Adversarial Threat Modeling: Conduct cutting-edge threat modeling focused on AI/ML vulnerabilities, engineering a comprehensive risk management framework tailored to an AI-powered life sciences platform. - Model Artifacts Protection: Partner with AI teams to establish a granular inventory, classification and protection framework for ML models’ weights, code, and training data. - Agentic AI & LLM Guardrails: Design and deploy scalable guardrails, robust sandboxing, and real-time monitoring controls for our extensive LLM ecosystem, including ADK, MCP, and autonomous agentic workflows. - Collaborative Security Engineering: Partner with ML researchers and platform engineers to design and implement robust security controls across the entire ML lifecycle—from data ingestion and training pipeline integrity to inference runtime controls. - Model-Driven Incident Response: Act as the primary technical expert for AI security incidents, deploying advanced machine learning techniques to automate threat hunting, anomaly detection, and signatureless mitigation specific to LLMs. - Compliance & Governance Automation: Bridge the gap between complex AI architectures and emerging global regulations (e.g., EU AI Act) by automating risk posture monitoring and continuous compliance metrics. - AI Safety Frameworks: Pioneer the practical implementation of Isomorphic Labs' AI Safety standards, working in close coordination with Legal, Compliance, and external partners like Google DeepMind. Skills and qualifications Essential - AI/ML Engineering: Deep conceptual and practical understanding of deep learning frameworks (e.g., JAX, PyTorch, TensorFlow), large-scale cloud training or inference infrastructure, and LLM ecosystems. - Adversarial ML Mindset: Strong familiarity with AI security threat vectors (prompt injection, model inversion, data poisoning) and frameworks like OWASP Top 10 for LLMs or MITRE ATLAS. - Agentic Frameworks Security: Proven experience securing agentic frameworks (ADK, MCP), agentic first infrastructure and implementing robust agent-to-agent (A2A) identity and access controls. - Infrastructure Security: Solid proficiency in cloud platform security (GCP preferred), including container security, multi-cloud/SaaS integrations, and strict network isolation boundaries. - Pragmatic Risk Assessment: Proven ability to define and execute pragmatic mitigation strategies that balance a rigorous security posture with the high-velocity needs of a world-class scientific research organization. - Software Development & Automation: Ability to write production-grade code (Python preferred) to build custom security tooling, automate telemetry collection, or enforce policy layers. - Translational Communication: Exceptional skills in navigating ambiguity, building trusted relationships with AI researchers, and clearly translating complex machine learning risks into actionable engineering tasks for leadership. Nice to have - AI Red Teaming: Experience conducting empirical vulnerability research or executing simulated attacks against LLMs, agent networks, and core ML backends. - Regulated Environments: Prior exposure to BioTech, Pharma, and/or Deep Tech industries where data integrity, intellectual property protection, and regulatory compliance (GxP) are paramount. - Education & Background: BSc, MSc, or PhD in Computer Science, Machine Learning, Cybersecurity, or a related quantitative field. - Advanced Certifications: Relevant security or cloud credentials (e.g., OSCP, Professional Cloud Security Engineer) or specialized training in ML security. Culture and values We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. Thoughtful Thoughtful at Iso is about curiosity, creativity and care. It is about good people doing good, rigorous and future-making science every single day. Brave Brave at Iso is about fearlessness, but it’s also about initiative and integrity. The scale of the challenge demands nothing less. Determined Determined at Iso is the way we pursue our goal. It’s a confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we. Together Together at Iso is about connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere. Creating an extraordinary company We believe that to be successful we need a team with a range of skills and talents. We're building an environment where collaboration is fundamental, learning is shared and every employee feels supported and able to thrive. We value unique experiences, knowledge, backgrounds, and perspectives, and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. Hybrid working It’s hugely important for us to share knowledge and build strong relationships with each other, and we find it easier to do this if we spend time together in person. This is why we follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on which team you’re in). If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for an initial screening call. Please note that when you submit an application, your data will be processed in line with our privacy policy . >> Click to view other open roles at Isomorphic Labs
Research Scientist (Machine Learning), London
Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease. The future is coming. A future enabled and enriched by the incredible power of machine learning. A future in which diseases are curtailed or cured starting with better and faster drug discovery. Come and be part of an interdisciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring and collaborative culture. The world we want tomorrow is the one we’re building today. It starts with the culture at this company. It starts with you. About Iso Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed. Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases. We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design. Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI. Research Scientist (Machine Learning), London Your impact As a Research Scientist in machine learning (ML), you will play an exciting role in building greenfield machine learning based models and algorithms that will power our platform to transform the drug discovery world as we know it. Working in a highly creative, fast-paced and interdisciplinary environment, you will be partnering with leading engineers and scientists to conceive, design, and develop cutting edge machine learning algorithms to unlock new modelling and predictive power which will be critical to the organisation’s success. You will draw upon your existing deep research experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems. Depending on your experience: You will create and lead projects, bringing together a variety of disciplined scientists and engineers to pursue some of the most ambitious modelling problems with deep learning - as well as providing technical mentorship and people management for others in the ML community at Isomorphic Labs You will be instrumental in leading greenfield machine learning based research projects, building the models, and algorithms that will power our platform to transform the drug discovery world as we know it. What you will do - Contribute to our research directions in machine learning by using your extensive knowledge of the field to apply world-leading ML algorithms to drug discovery. - Identify and create novel ML techniques and the required data to train. - Develop the architectures and training algorithms of machine learning models. - Analyse and tune experimental results to inform future experimental directions. - Implement and scale training and inference engineering frameworks. - Report and present research findings and developments clearly and efficiently, to both other ML scientists and scientists of different disciplines. - Iterate collaboratively with scientists and domain experts, sharing your own domain experience. - Suggest and engage in team collaborations to meet ambitious research goals. Depending on your experience: - Provide technical mentorship and guidance to the ML research community, advising on projects, and shaping our research roadmap based on your deep technical expertise. - Provide developmental support to other ML research scientists. - Create, lead, and run ML research projects, fostering collaborative and diverse teams to solve high priority modelling problems.Cultivate a diverse and inclusive research culture. Skills and qualifications Essential - PhD or equivalent practical experience in a technical field. - A proven track record in machine learning using deep learning techniques, including designing new architectures, hands-on experimentation, analysis, and visualisation. - Strong knowledge of linear algebra, calculus and statistics. - Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas. - A passion for applying ML research to real world problems. - Depending on your experience: project supervision, leadership, or management. Nice to have - PhD in machine learning or computer science. - Relevant research experience to the position such as post doctoral roles, a proven track record of publications, or contributions to machine learning codebases. - Experience working in a scientific environment across disciplines (particularly biology, chemistry, physics). - Experience working with biological or chemical data and biological or chemistry software. - Experience working with real-world datasets. - Experience with ML on accelerators. - Experience in any of: large scale deep learning, generative models, graph neural networks, deep learning for drug discovery, deep learning for computer vision, 3D graphics/robotics, real-world applied RL. Culture and values We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. Thoughtful Thoughtful at Iso is about curiosity, creativity and care. It is about good people doing good, rigorous and future-making science every single day. Brave Brave at Iso is about fearlessness, but it’s also about initiative and integrity. The scale of the challenge demands nothing less. Determined Determined at Iso is the way we pursue our goal. It’s a confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we. Together Together at Iso is about connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere. Creating an extraordinary company We believe that to be successful we need a team with a range of skills and talents. We're building an environment where collaboration is fundamental, learning is shared and every employee feels supported and able to thrive. We value unique experiences, knowledge, backgrounds, and perspectives, and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. Hybrid working It’s hugely important for us to share knowledge and build strong relationships with each other, and we find it easier to do this if we spend time together in person. This is why we follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on which team you’re in). If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for an initial screening call. Please note that when you submit an application, your data will be processed in line with our privacy policy . >> Click to view other open roles at Isomorphic Labs
AI Research Engineer - 3D Computer Vision
Who we are Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards. As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously. We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications. The role At Helsing we deliver AI-based capabilities and the enabling foundation that allow machines to perceive and assist human decision-making. You will have the unique opportunity to shape AI capabilities in one of the most challenging sectors, where high generalisation capabilities need to be paired with hardware constraints and robustness against adversarial attacks. You will be part of a computer vision team specialising in scene understanding and localisation, responsible for building systems for scene matching, geo-registration, simultaneous localisation and mapping (SLAM), and 3D reconstruction. You will develop and extend state-of-the-art architectures and pipelines, design rigorous experiments, and conduct benchmarks to evaluate and improve real-world performance, including adaptation to the compute constraints and operational requirements of downstream deployments. You will collaborate across research, engineering, and product teams on high-impact projects. Typical examples of day-to-day responsibilities include, depending on the team: - Developing and benchmarking scene matching and geo-registration pipelines robust to challenging conditions such as changes in illumination, viewpoint, season, or sensor modality. - Building and evaluating SLAM, visual odometry, or structure-from-motion systems for deployable platforms under real-world operational constraints. - Researching and adapting state-of-the-art geometric deep learning or feature matching methods to concrete use cases, from local feature descriptors to learned place recognition. - Collaborating with product and downstream deployment teams to integrate localisation and reconstruction outputs into navigation or decision-making pipelines and shape the roadmap for new capabilities. The day-to-day You will develop computer vision models and pipelines that leverage and extend the latest state-of-the-art methods and architectures, as well as design experiments and conduct benchmarks to evaluate and improve their performance in real-world scenarios. You will also apply and develop techniques to adapt them to the target hardware and constraints associated to the downstream ML/AI tasks. You will contribute to impactful projects and will collaborate with people across several teams and backgrounds. You should apply if you - Hold an MSc in computer science, machine learning, robotics, or a closely related field, with experience in designing, implementing, and thoroughly evaluating advanced AI-based systems. - Have hands-on experience developing localisation, scene matching, or 3D reconstruction systems. You have iterated on models and geometric pipelines beyond benchmarks and understand what it takes to make these systems reliable under real-world data distributions and deployment constraints. - Are deeply familiar with modern approaches to geometric computer vision and deep learning, including learned feature matching, place recognition, visual SLAM, visual-inertial odometry (VIO), or neural 3D representations such as neural radiance fields (NeRF), and have applied techniques such as domain adaptation or model compression to practical problems. - Possess solid software engineering skills, writing clean and well-structured code in Python, and have experience deploying AI software to production including testing, QA, and monitoring. - Have excellent communication skills and the ability to report and present research findings clearly and efficiently, both internally and externally. - Are passionate about keeping up to date with current research and enjoy reimplementing and extending state-of-the-art approaches in geometric deep learning, SLAM, and 3D vision. Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points. Nice to have - PhD in computer vision, machine learning, robotics, or a related field, with publications in top-tier venues (e.g. CVPR, NeurIPS, ICLR, ICCV, ICRA, IROS, ECCV). - Experience designing, evaluating, and delivering end-to-end AI systems on edge devices with constrained compute resources. - Experience with simulators, emulators, or synthetic data generation pipelines for geometry or localisation tasks. - Experience with Rust and/or C++. Join Helsing and work with world-leading experts in their fields - Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns. - The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world. - Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defence industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward. - In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts. - We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practise responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about. What we offer - Competitive salary and VSOP options - Relocation support : up to €2,500 and 4 weeks temporary accommodation - Learning : €500/£450 yearly allowance - Health & wellness : gym membership and mental health support (Nilo.health) - Social : regular company events and monthly social allowances - Enhanced parental leave : 22 weeks fully paid for primary caregivers & 6 weeks for secondary caregivers - Family support: 5 days of paid family emergency leave, 100% remote work option during pregnancy and phased return to work These are the core benefits across all locations, there may be additional benefits in certain locations. Helsing is an equal opportunities employer. We are committed to equal employment opportunity regardless of race, religion, sexual orientation, age, marital status, disability or gender identity. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation Helsing's Candidate Privacy and Confidentiality Regime can be found here .
AI Research Engineer
About Graphcore At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence. Job Summary As a research engineer at Graphcore, you will contribute to the advancement of AI research, investigating new ideas that push the limits on important AI/ML problems. Specialised hardware has been the key driver of the progress of AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to advancing this exciting field. We are therefore looking for individuals who combine strong machine learning experience with practical engineering skills to deliver impactful AI research. We are seeking AI researchers with strong software engineering experience, particularly in lower-level programming and performance optimisation for hardware efficiency. Our research spans a broad range of topics, including efficient training and inference, world models, life sciences, reinforcement learning, and beyond. You will work closely with researchers to generate ideas and translate them into scalable implementations, contributing to publications and projects that help to steer the future of AI hardware. The Team Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world. We pride ourselves on being a supportive and collaborative team, where we organise around our individual research interests to solve problems together in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications, including for sequence- and graph-based data. We’re based across London, Cambridge and Bristol, with projects and discussions that involve all our locations. Perhaps the best way to get an idea of what we’re all about is to read one of our papers or an article on our blog . If you’re excited to work at the cutting edge of AI supported by new hardware and want to develop your skills in this area, we’d love to hear from you! Responsibilities and Duties - Generate AI/ML ideas, design experiments, implement them & evaluate results. - Prepare, submit & present your work to AI conferences and workshops. - Provide technical insight to internal teams by designing experiments and delivering clear, actionable reports. - Collaborate with researchers, silicon and software engineers at Graphcore to help define, build and test Graphcore’s next generation of AI hardware. About you: Essential: - Master’s, PhD or equivalent experience in a technical discipline (e.g., Maths, Statistics, Computer Science, Physics, Chemistry). - Python programming in a modern deep learning framework, e.g. PyTorch or JAX. - Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling. - Capable of designing, executing and reporting from ML experiments. - Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton. - Practical familiarity with hardware capabilities for deep learning – threads, caches, vector & matrix engines, data dependencies, bus widths and throttling. - Practical familiarity with software stacks for deep learning – compilation, kernel fusion, XLA/ATen ops, streams, and asynchronous execution Desirable: - Mathematics skills to support the above: calculus, probability theory and linear algebra. - Experience submitting papers to international scientific conferences or workshops. Benefits In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments. Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications
Algorithm - AI Research Engineer Intern
About FuriosaAI FuriosaAI builds high-performance, high-efficiency AI compute for the Inference Era. Founded in 2017 by veteran semiconductor and AI algorithm engineers, Furiosa operates globally with offices in Korea and Silicon Valley, along with a compiler-focused R&D lab in Lisbon. Our vision is to make AI computing sustainable, enabling access to powerful AI for everyone on Earth. We solve the AI hardware energy and operational cost crisis at the architectural level, rather than through brute force, building the world's first truly AI-native compute platform to unlock the full potential of artificial intelligence for every enterprise. About FuriosaAI and AI Research Part FuriosaAI는 자사의 고성능 AI 반도체를 기반으로, 독자적인 AI 모델까지 함께 제공하는 full-stack frontier AI company를 지향합니다. AI Research Part는 이러한 frontier AI 비전을 실현하기 위해 모인 팀입니다. 본 포지션에서는 NPU 관련 경험 여부보다는, 뛰어난 AI 연구 역량과 이를 실제 모델 및 시스템으로 구현하는 능력에 초점을 맞추고 있습니다. FuriosaAI와 함께 독자 AI 모델 개발 및 frontier AI 연구를 수행하고, 그 성과를 글로벌 AI impact로 연결할 분을 찾습니다. Research Focus AI Research Part는 현재 다음 세 가지 연구 축에 집중하고 있습니다. - Coding, tool-use 등 agentic task에 특화된 독자 LLM을 개발합니다. - 해외 유수 연구기관과 공동 연구를 수행하며, FuriosaAI의 frontier AI 연구 역량과 글로벌 연구 네트워크를 확장합니다. - 추론(rollout)이 큰 비중을 차지하는 LLM RL post-training에서 FuriosaAI의 NPU를 활용하는 것을 목표로, 비동기 RL pipeline을 개발하고 독자 LLM post-training 역량을 확보합니다. AI Research Part는 현재 LLM 연구에 집중하고 있으며, 합류 후에는 주로 LLM 관련 프로젝트를 수행하게 됩니다. 다만 기존 경험이 LLM 분야에 한정될 필요는 없습니다. Computer Vision을 포함한 다양한 AI 분야에서 우수한 연구·개발 경험을 쌓아오신 분들을 환영합니다. Minimum Qualifications - AI 관련 분야의 석사 과정에 재학 중이거나, 이에 준하는 연구·개발 경험이 있으신 분 - PyTorch 등 주요 Deep Learning Framework 기반의 AI 기술 개발 경험이 있으신 분 - 해외 출장에 결격 사유가 없으신 분 Preferred Qualifications - ML / CV / NLP 분야에서 Top-Tier 학회 논문 출판을 주도한 경험이 있으신 분 - Coding LLM, tool-use agent 등 agentic AI 관련 연구 또는 개발 경험이 있으신 분 - LLM post-training 관련 연구 또는 개발 경험이 있으신 분 - AI 관련 오픈소스 프로젝트에 기여한 경험이 있으신 분 - 해외 기관과 원활한 의사소통이 가능하신 분 Why Join FuriosaAI The defining bottleneck of the AI era is building the right hardware and software stack to run it at global scale. Furiosa is solving this challenge holistically from the ground up. With our flagship chip, RNGD, in mass production today and our next-generation platform in development with Broadcom, we are proving that full-stack, tensor-native compute is the future of AI infrastructure. This is a pivotal moment to join our team, right as we accelerate our global expansion. At Furiosa, you will: Solve AI’s Most Urgent Challenge. Help build the high-performance, energy-efficient inference hardware and software required to fulfill the promise of advanced AI. Pioneer Full-Stack Co-Design. Work with teams that are architecting solutions from silicon up through the compiler (featuring innovations like Tensor Contraction Language and Virtual ISA) and serving frameworks. Ship Real-World Silicon, Software, and Solutions. Turn breakthrough technology into commercial deployment. RNGD is in mass production with TSMC and running live enterprise workloads for global leaders like LG AI Research and Samsung SDS. Partner With the Industry's Best. Collaborate across an elite global ecosystem that includes TSMC, Broadcom, SK Hynix, and GUC. Do Your Life’s Best Work. Join a brilliant, low-ego, mission-driven team in a high-trust environment that values autonomy, intellectual curiosity, and shared ambition. Contact - recruit@furiosa.ai
Algorithm - AI Research Engineer
About FuriosaAI FuriosaAI builds high-performance, high-efficiency AI compute for the Inference Era. Founded in 2017 by veteran semiconductor and AI algorithm engineers, Furiosa operates globally with offices in Korea and Silicon Valley, along with a compiler-focused R&D lab in Lisbon. Our vision is to make AI computing sustainable, enabling access to powerful AI for everyone on Earth. We solve the AI hardware energy and operational cost crisis at the architectural level, rather than through brute force, building the world's first truly AI-native compute platform to unlock the full potential of artificial intelligence for every enterprise. About FuriosaAI and AI Research Part FuriosaAI는 자사의 고성능 AI 반도체를 기반으로, 독자적인 AI 모델까지 함께 제공하는 full-stack frontier AI company를 지향합니다. AI Research Part는 이러한 frontier AI 비전을 실현하기 위해 모인 팀입니다. 본 포지션에서는 NPU 관련 경험 여부보다는, 뛰어난 AI 연구 역량과 이를 실제 모델 및 시스템으로 구현하는 능력에 초점을 맞추고 있습니다. FuriosaAI와 함께 독자 AI 모델 개발 및 frontier AI 연구를 수행하고, 그 성과를 글로벌 AI impact로 연결할 분을 찾습니다. Research Focus AI Research Part는 현재 다음 세 가지 연구 축에 집중하고 있습니다. - Coding, tool-use 등 agentic task에 특화된 독자 LLM을 개발합니다. - 해외 유수 연구기관과 공동 연구를 수행하며, FuriosaAI의 frontier AI 연구 역량과 글로벌 연구 네트워크를 확장합니다. - 추론(rollout)이 큰 비중을 차지하는 LLM RL post-training에서 FuriosaAI의 NPU를 활용하는 것을 목표로, 비동기 RL pipeline을 개발하고 독자 LLM post-training 역량을 확보합니다. AI Research Part는 현재 LLM 연구에 집중하고 있으며, 합류 후에는 주로 LLM 관련 프로젝트를 수행하게 됩니다. 다만 기존 경험이 LLM 분야에 한정될 필요는 없습니다. Computer Vision을 포함한 다양한 AI 분야에서 우수한 연구·개발 경험을 쌓아오신 분들을 환영합니다. Minimum Qualifications - AI 관련 분야에서 석사 학위 소지자 또는 이에 준하는 경험이 있으신 분 - PyTorch 등 주요 Deep Learning Framework 기반의 AI 기술 개발 경험이 있으신 분 - 해외 출장에 결격 사유가 없으신 분 Preferred Qualifications - ML / CV / NLP 분야에서 Top-Tier 학회 논문 출판을 주도한 경험이 있으신 분 - AI 연구 및 개발 분야에서 박사 학위 혹은 2년 이상 산업 경험이 있으신 분 - Coding LLM, tool-use agent 등 agentic AI 관련 연구 또는 개발 경험이 있으신 분 - LLM post-training 관련 연구 또는 개발 경험이 있으신 분 - AI 관련 오픈소스 프로젝트에 기여한 경험이 있으신 분 - 해외 기관과 원활한 의사소통이 가능하신 분 Why Join FuriosaAI The defining bottleneck of the AI era is building the right hardware and software stack to run it at global scale. Furiosa is solving this challenge holistically from the ground up. With our flagship chip, RNGD, in mass production today and our next-generation platform in development with Broadcom, we are proving that full-stack, tensor-native compute is the future of AI infrastructure. This is a pivotal moment to join our team, right as we accelerate our global expansion. At Furiosa, you will: Solve AI’s Most Urgent Challenge. Help build the high-performance, energy-efficient inference hardware and software required to fulfill the promise of advanced AI. Pioneer Full-Stack Co-Design. Work with teams that are architecting solutions from silicon up through the compiler (featuring innovations like Tensor Contraction Language and Virtual ISA) and serving frameworks. Ship Real-World Silicon, Software, and Solutions. Turn breakthrough technology into commercial deployment. RNGD is in mass production with TSMC and running live enterprise workloads for global leaders like LG AI Research and Samsung SDS. Partner With the Industry's Best. Collaborate across an elite global ecosystem that includes TSMC, Broadcom, SK Hynix, and GUC. Do Your Life’s Best Work. Join a brilliant, low-ego, mission-driven team in a high-trust environment that values autonomy, intellectual curiosity, and shared ambition. Contact - recruit@furiosa.ai
Senior Machine Learning Engineer, Applied Intellig...
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. ABOUT THE TEAM Maritime Digital Production (MDP) is the software and digital systems function within Anduril's Heavy Metal division. We build and deploy the full technology stack that powers Anduril's shipbuilding factories: the data infrastructure that makes every machine and sensor visible in real time, the manufacturing execution system (ArsenalOS) that workers and planners use every shift, the scheduling engine that replans production in minutes instead of days, and the AI systems that eliminate manual toil from both the shop floor and business operations. MDP operates at the boundary between Operational Technology and Information Technology. Our systems live where factory-floor machines, edge compute, and OT networks meet enterprise platforms and cloud infrastructure. We incubate solutions close to the production line, validate them with real operators building real hardware, harden them for reliability and security, and then scale them across multiple sites. The environment is fast, physical, and consequential. When our systems go down, production stops. The output of our work is not a dashboard: it is a ship. This is not a support function. It is a strategic investment by Anduril in the premise that digitizing the manufacturing lifecycle end-to-end, from engineering definition through scheduling through execution through field feedback, is how Heavy Metal will out-build, out-adapt, and out-scale the traditional defense industrial base. MDP is scaling from a founding team to 70+ engineers across multiple U.S. sites. You will be joining early, working on hard problems with real operational stakes, and shaping how manufacturing software is built at Anduril from the ground up. ABOUT THE JOB As a Senior ML Engineer on the Applied Intelligence initiative, you will help architect and operate the AI/ML platform stack that powers ML pipelines for factory sensing, document processing, and intelligent automation. You will help build the infrastructure that operationalizes computer vision, NLP, and RAG-enabled tools, translating factory scenarios into production-grade AI workflows with clear human-in-the-loop controls and enterprise system integrations. WHAT YOU'LL DO - Architect and own the AI/ML platform stack—from data ingestion, labeling, and feature engineering to model training, deployment, monitoring, and lifecycle management for factory sensing and intelligent automation applications. - Select, prioritize, and standardize industrial AI components including feature stores, vector databases for RAG pipelines, OCR/IDP and computer vision model serving, orchestration layers, and observability systems. - Build model-serving and inference frameworks optimized for production environments, supporting real-time and batch execution across cloud, edge, and shop-floor systems. - Partner with manufacturing engineers and factory operators to understand production workflows and translate them into MLOps requirements. - Write production-quality code with comprehensive tests, participating in code review and architectural discussions. - Translate factory scenarios (quality inspection, receiving, root-cause analysis, document processing) into applied AI workflows with defined human-in-the-loop gates, audit trails, and integration contracts with PLM, MES, ERP, and the unified data plane. - Implement event-driven data pipelines and telemetry systems that feed models with contextualized, real-time signals from factory sensors, production systems, and logistics operations. - Deploy and operate your systems in factory environments, including edge compute clusters and OT networks. - Drive make/buy strategy by researching internal and vendor AI capabilities and recommending investments aligned to enterprise roadmaps, Anduril IP principles, and production constraints. - Define and maintain model governance processes for validation, safety reviews, traceability, and rollback procedures for AI systems in production. - Lead reliability engineering for deployed models—managing drift detection, retraining triggers, alerting, and operational SLOs for factory sensing and document processing applications. - Leverage AI tooling (coding assistants, automation) in your development workflow and contribute to team engineering practices. - Join an on-call rotation supporting production factory systems. - Mentor junior engineers and data scientists; establish best practices for MLOps, observability, data management, and secure handling of sensitive production data. REQUIRED QUALIFICATIONS - 8+ years of experience in a software engineering role building production systems, ideally in a fast-paced environment. - Deep expertise in MLOps with end-to-end experience delivering production-grade AI/ML systems. - Strong technical fluency in modern software architectures, APIs, distributed systems, CI/CD, and cloud or edge infrastructure. - Deep experience with MLOps: data acquisition, labeling, curation, pipeline management, model versioning, continuous integration, and model monitoring. - Strong proficiency in Python and experience with deep learning frameworks (PyTorch, TensorFlow). - Experience building and deploying containerized ML services using Docker and Kubernetes. - Proficiency in data engineering, time-series data modeling, and working with semantic/ontology-driven data systems. - Experience implementing observability for model performance, inference accuracy, and data drift. - Familiarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration. - Experience building systems that must operate reliably under real-world operational constraints (high availability, low latency, or constrained environments). - Strong stakeholder management skills with proven experience aligning engineering, data, and manufacturing teams. - Excellent written and verbal communication skills; able to bridge research, platform, and production domains and collaborate across engineering, manufacturing, and operations teams. - Degree in Computer Science, Information Systems, Engineering, or related technical field, or equivalent practical experience. - U.S. Person status is required as this position needs to access export controlled data. PREFERRED QUALIFICATIONS - Experience in manufacturing, industrial, or OT-adjacent domains (MES, SCADA, PLC integration, factory automation, IoT). - Experience applying AI/ML within manufacturing, logistics, industrial control, or production environments. - Background with digital twins, predictive maintenance, OCR/IDP, computer vision, or speech-to-text model integrations. - Experience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal. - Familiarity with GPU acceleration (CUDA) and inference optimization (TensorRT, Triton Inference Server). - Experience building RAG (Retrieval-Augmented Generation) systems, vector databases (Pinecone, Weaviate, Milvus), and LLM deployment pipelines. - Familiarity with frontier AI tooling, AI coding assistants, and AI-enabled software development workflows. - Experience in hyper-growth startup-like environments, with demonstrated success balancing speed, ambiguity, and long-term system health. - Familiarity with enterprise systems such as ERP, MES, WMS, PLM, or manufacturing planning systems. - Experience in regulated environments (NNPI/ITAR) and secure model/data governance. - Demonstrated ability to mentor engineers and set technical direction for AI/ML infrastructure at scale. - Experience with MLOps tools including experiment tracking (MLflow, Weights & Biases), feature stores (Feast, Tecton), and model registries. - Experience in manufacturing industries with hands-on exposure to assembly lines or production environments. - Knowledge of edge ML deployment, model optimization (quantization, pruning), or deploying models on resource-constrained devices. - Eligible to obtain and maintain a U.S. Secret security clearance. US Salary Range $220,000 - $292,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
Research Scientist (Generative Modeling)
ABOUT WORLD LABS World Labs https://www.worldlabs.ai/ is a frontier AI research and product company advancing spatial intelligence, the next frontier beyond large language models. Co-founded by Dr. Fei-Fei Li https://profiles.stanford.edu/fei-fei-li, Justin Johnson https://www.linkedin.com/in/justin-johnson-41b43664/ and Ben Mildenhall https://www.linkedin.com/in/ben-mildenhall-86b4739b/, the company is pioneering world models that perceive, generate, reason, and interact with virtual and physical worlds. The company’s flagship product, Marble https://marble.worldlabs.ai/, transforms text, images, and video into fully navigable 3D worlds, unlocking applications across gaming, film, architecture, robotics, and immersive digital experiences. Backed by leading investors and with over $1B raised, World Labs is assembling a world-class team at the intersection of AI research and real-world deployment. ROLE OVERVIEW We are looking for a talented Research Scientist with a strong background in generative modeling, particularly diffusion models, to join our modeling team. This role is ideal for candidates with deep expertise in diffusion models applied to images, videos, or 3D assets and scenes. While not required, experience in one or more of the following areas is a strong plus: - Large-scale model training - Data curation for pre-training or post-training - Tokenizers and VAEs for image, video, or 3D data - Long-context architectures - 3D vision You will collaborate closely with researchers, engineers, and product teams to bring advanced 3D modeling and machine learning techniques into real-world applications, ensuring that our technology remains at the forefront of visual innovation. This role involves significant hands-on research and engineering work, driving projects from conceptualization through to production deployment. WHAT YOU WILL DO - Design, implement, and train large-scale diffusion models for generating 3D worlds - Develop and experiment with large-scale diffusion models to add novel control signals, adapt to target aesthetic preferences, or distill for efficient inference - Collaborate closely with research and product teams to understand and translate product requirements into effective technical roadmaps. - Contribute hands-on to all stages of model development including data curation, experimentation, evaluation, and deployment. - Continuously explore and integrate cutting-edge research in diffusion and generative AI more broadly - Act as a key technical resource within the team, mentoring colleagues, and driving best practices in generative modeling and ML engineering KEY QUALIFICATIONS - 3+ years of experience in generative modeling or applied ML roles, ideally at a startup or other fast-paced research environment - Extensive experience with machine learning frameworks such as PyTorch or TensorFlow, especially in the context of diffusion models and other generative models - Deep expertise in at least one area of generative modeling: pre-training, post-training, diffusion distillation, fine-tuning with new conditioning signals, etc for diffusion models - Strong history of publications or open-source contributions involving large-scale diffusion models - Strong coding proficiency in Python and experience with GPU-accelerated computing. - Ability to engage effectively with researchers and cross-functional teams, clearly translating complex technical ideas into actionable tasks and outcomes. - Comfortable operating within a dynamic startup environment with high levels of ambiguity, ownership, and innovation. PREFERRED QUALIFICATIONS - Contributions to open-source projects in the fields of computer vision, graphics, or ML. - Familiarity with large-scale training infrastructure (e.g., multi-node GPU clusters, distributed training environments). - Experience integrating machine learning models into production environments. - Led or been involved with the development or training of large-scale, state-of-the-art generative models WHO YOU ARE - Fearless Innovator: We need people who thrive on challenges and aren't afraid to tackle the impossible. - Resilient Builder: Impacting Large World Models isn't a sprint; it's a marathon with hurdles. We're looking for builders who can weather the storms of groundbreaking research and come out stronger. - Mission-Driven Mindset: Everything we do is in service of creating the best spatially intelligent AI systems, and using them to empower people. - Collaborative Spirit: We're building something bigger than any one person. We need team players who can harness the power of collective intelligence. We're hiring the brightest minds from around the globe to bring diverse perspectives to our cutting-edge work. If you're ready to work on technology that will reshape how machines perceive and interact with the world - then World Labs is your launchpad. Join us, and let's make history together. Logistics: Location: This role is based in San Francisco, California; we work in-person in the office 5 days a week. Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. Benefits: We offer generous health, dental, vision and 401K benefits, flexible PTO, paid parental leave, and relocation support as needed. Equal Employment Opportunity World Labs is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected under applicable law. We welcome all qualified applicants and are committed to providing reasonable accommodations throughout the hiring process upon request.
Staff Machine Learning Engineer – VLM/LLM
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. This role follows a hybrid work schedule and you will report to a Senior Staff Software Engineer. You will: - Work with a creative team of people who help to build the state-of-the-art Foundation Models that are used throughout Waymo’s systems, both onboard autonomous vehicles and offboard in simulation - Lead the development of end-to-end evaluation systems and benchmarks for Waymo Foundation models, encompassing the entire lifecycle from pretraining and supervised fine-tuning (SFT) to reinforcement learning (RL), for evaluating the quality, safety, and realism of embodied AI agents - Partner within and across organizations to land disruptive and innovative tech in production - Implement and extend large large scale data and evaluation pipelines You have: - Master’s degree or PhD degree in Computer Science, similar technical field of study, or equivalent practical experience - 5+ years of experience in ML engineering and applied Deep Learning, with a strong portfolio of shipped products or publication record - Experience with large scale distributed system - Proficient programming skills (eg: Python, C/C++) - Strong analytical and debugging skills We prefer: - ML infra experience: training, evaluating and deploying ML models at scale - Deep learning experience, especially with generative models, e.g., LLMs/VLMs, and/or reinforcement learning - Proficiency and in-depth knowledge of the inner workings of an ML framework (e.g. Pytorch, JAX, Tensorflow) In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include: - Health, dental, vision, life, disability insurance - Retirement Benefits: 401(k) with company match - Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment - Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary) - Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks - Baby Bonding Leave: 18 weeks - Holidays: 13 paid days per year The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $238,000 - $302,000 USD
AI Research Engineer, Post-Training
TL;DR: Lovable lets over 2 million people build software using plain language, and the models behind it need to be exceptional. We're hiring an engineer who has gotten their hands dirty with post-training at scale and wants to do it again for one of the fastest-growing AI products in the world. You'll own our full post-training pipeline: translating the latest research into production training recipes, adapting them for code generation and agent workloads, and putting improved models in front of users fast. The goal is to get promising research into production within days or weeks, not months. This isn't an academic research position - you'll spend as much time in production infrastructure as in training configs, and your success is measured by what ships. Why Lovable? Lovable is the software creation platform that gives people the power to act on the problems closest to them. For decades, turning an idea into software required so much capital, technical fluency, and time that many ideas never came to life. Lovable is the counterargument: a platform for all people with ideas, ambition, and problems worth solving. From solopreneurs to small business owners to teams at companies like Adidas and Zendesk, people have built over 60 million projects on Lovable since its launch in November 2024. And we’re just getting started. We’re building a generational company from Stockholm, with growing teams in London, Boston, New York, and San Francisco. Our team is small, talent-dense, and moving quickly, with a culture rooted in extreme ownership, high velocity, and low-ego collaboration. We look for people who care deeply, ship fast, and are eager to make a dent in the world. Lovable is one of TIME’s 100 Most Influential Companies and has been recognized on the Forbes AI 50 and CNBC Disruptor 50, reflecting our momentum as one of Europe’s fastest-growing AI companies and one of the most ambitious places to build in this next era of software. What we’re looking for - You've personally run post-training jobs on large language models - RFT/RLVR, preference optimization, or similar. Not just called APIs or written prompts, but actually trained and iterated on models - You can write solid production code. The systems you build need to run reliably, not just produce interesting research artifacts - You're fluent in at least one major ML framework (PyTorch, JAX) and comfortable working with distributed training setups and GPU clusters - You understand the math behind preference optimization, reward modeling, and alignment techniques - and can reason about when each approach fits - You've built or significantly contributed to evaluation systems that capture real-world quality, not just benchmark scores - You can trace a model quality regression from user-facing symptoms back through serving, inference, and training - and you enjoy doing it - You want to ship. Research taste matters, but at Lovable the question is always "how fast can we get this to users?" Preferred: - You've worked on code generation or agentic use cases specifically - You've put post-trained models into the hands of real users and seen how they hold up at scale - You've owned the full loop: curating data, running training, evaluating results, deploying, and monitoring in production - You have a habit of reading a paper on Monday and having a prototype running by Friday - You've experimented with speculative decoding or similar techniques to improve model efficiency - You have strong views on evaluation methodology and have built evals that actually predict user satisfaction - You've published or contributed meaningfully to the open-source ML ecosystem What you’ll do - Own the full lifecycle of Lovable's post-training pipeline - from data curation and training runs through evaluation and deployment - Apply and adapt reinforcement learning, preference optimization, and supervised fine-tuning methods to make our models better at generating code, reasoning about user intent, and acting as reliable agents - Build the evaluation and experimentation infrastructure that tells us whether a model change actually helps users - covering helpfulness, safety, latency, and reliability - Develop and operate the production systems that run training jobs at scale, including GPU orchestration and data pipelines - Work across team boundaries with our agent, product, and infrastructure engineers to turn model gains into product improvements users can feel - Investigate and resolve failures end-to-end - whether the root cause is in a training recipe, a data issue, or a serving regression - Read papers, run experiments, and move fast: the goal is to get promising research into production within days or weeks, not months About your application Please submit your application in English. It’s our company language, so you’ll be speaking lots of it if you join. We treat all candidates equally - if you’re interested, please apply through our careers portal.
ML Scientist, Foundation Models for Life Sciences
Your Impact at Lila Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), the Foundation Models team builds foundation models that learn across biological sequence, molecular structure, and experimental data to power automated scientific discovery across Lila's life science domains. We are seeking a Scientist I or II to work on structure prediction and co-folding. The team's current emphasis is protein–protein and complex prediction in support of antibody and biologics design, and on making those predictions good enough to drive real experimental decisions. You will contribute across problem formulation, model design, training, evaluation, and integration into Lila's closed-loop discovery engine. This is an IC role for someone building deep expertise in structure-aware generative AI for biology. You will own research sub-problems end to end, collaborate closely with experimental scientists to close the computational–experimental loop, and contribute to Lila's presence in the broader scientific community. What You'll Be Building - Train and evaluate structure prediction and co-folding models for protein complexes, protein–protein interactions, and related biomolecular systems - Build and extend models informed by AlphaFold-style co-folding, diffusion models, protein language models, and related structure-aware ML methods - Build rigorous evaluation frameworks to ensure model generalization to challenging de novo design problems - Scale training, inference, and evaluation workflows across large GPU clusters - Be part of the end-to-end ML process within Lila's “Lab-in-the-Loop” lifecycle: shape data generation strategy, build pipeline models, and design feedback loops where experimental results improve model performance - Contribute to adjacent foundation model research where it strengthens the structural work, including biological sequence design and multimodal scientific reasoning - Translate biological questions into well-defined ML problems and interpret model outputs alongside wet-lab scientists, structural biologists, and computational biologists - Support research quality and methodology standards within the foundation models program What You’ll Need to Succeed - PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, or a related quantitative field (or Master's with equivalent research experience) - Hands-on experience training deep learning models on molecular, protein, or structural data - Strong foundation in generative model architectures and training, with demonstrated ability to design careful experiments, ablations, and evaluations - Ability to formulate and execute research independently, from problem definition through experimentation - Familiarity with at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or related) - Experience collaborating with experimental scientists or working with biological/chemical data - Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with GPU-based training workflows Bonus Points For - Experience training or extending co-folding, structure prediction, protein–protein, or diffusion deep learning models - Experience with AlphaFold or AlphaFold-derived methods (e.g., Boltz, Protenix), RFdiffusion, or protein language models - Antibody, biologics, or protein design experience, including structure-guided optimization - Familiarity with distributed training infrastructure and large-scale scientific data pipelines - Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications - Experience with active learning loops or closed-loop experimental workflows - Experience integrating ML models into agentic scientific workflows - High-impact publications or open‑source contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAI, Nature Methods, Nature Biotechnology, or equivalent) Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program. International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market. Expected Base Salary Range $176,000 - $304,000 USD About LILA Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy . A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
Research Engineer, AI/ML Systems
Who We Are Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction. Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in. We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute. The Way We Work The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice: - Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping. - Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through. - Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together. - Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work. - Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most. - Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact. What We're Looking For We're looking for a curious, adaptable Research Engineer who enjoys solving difficult technical problems and building across the AI stack to join our Research Engineering function here at Lightning. This role is intentionally broad, with a primary focus on post-training models and the systems that support it. You’ll work across ML engineering, software engineering, and AI systems to improve how we develop, train, evaluate, and deploy models. As team priorities evolve, you’ll have opportunities to contribute across developer tooling, infrastructure, and platform capabilities that help researchers and customers develop, train, and deploy AI more effectively. We're looking for someone who enjoys learning new technologies, working across multiple technical domains, and tackling whatever problems have the greatest impact. Strong software engineering fundamentals, curiosity, and a willingness to continuously learn are more important than already being an expert in every area of AI systems. If you've spent meaningful time building AI projects, experimenting with PyTorch, contributing to open source, reproducing research, or exploring new ideas because you're genuinely interested, we'd love to hear about it. This role is hybrid with a minimum of 2 in-office days per week in San Francisco, Seattle, NYC, or London, with fully remote work considered for candidates outside of our office hub locations. All employees participate in occasional team and company offsites. What You'll Do - Develop and post-train models, while building and improving the systems and workflows needed to run, evaluate, debug, and scale training workloads. - Build software, tooling, and platform capabilities that improve how researchers, developers, and customers develop, train, and deploy AI systems. - Contribute to Lightning’s open-source projects by building new features, improving existing functionality, and collaborating with the broader developer community. - Work across deep learning systems, developer tooling, backend services, and platform infrastructure to solve a wide variety of engineering challenges. - Collaborate directly with customers to understand real-world AI workloads, investigate technical challenges, and translate those learnings into reusable product and platform improvements. - Prototype new ideas, evaluate approaches, and turn successful experiments into production-quality software. - Partner closely with research, product, and infrastructure engineering teams to improve developer experience, AI workflows, and platform capabilities. - Debug complex technical problems spanning machine learning, distributed systems, backend software, and developer tooling. - Learn new technologies quickly and contribute wherever your skills can have the greatest impact as team priorities evolve. What You’ll Need Required Qualifications - Experience building, training, evaluating, or experimenting with deep learning models. - Hands-on experience with deep learning frameworks such as PyTorch. - Strong software engineering fundamentals building software and debugging and problem-solving skills, with the ability to investigate unfamiliar technical challenges. - Curiosity, initiative, and a demonstrated ability to quickly learn new technologies and technical domains. - Excellent communication and collaboration skills, including the ability to work effectively across research, product, infrastructure, and customer-facing engagements. - Comfortable working in fast-moving, ambiguous environments where priorities evolve over time. - Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. Ideal Experience - Experience with model training at scale, including distributed training, performance optimization, training stability, and/or large-scale experimentation. - Experience with transformer-based language models or modern generative AI systems. - Experience with distributed systems, cloud infrastructure, or large-scale machine learning workloads. - Familiarity with technologies such as CUDA, Hugging Face, DeepSpeed, FSDP, Triton, vLLM, SGLang, NVIDIA Molt, or related AI infrastructure tooling. - Experience contributing to open-source software or conducting research through academia, industry, or meaningful independent projects. - Startup experience or experience working on highly cross-functional engineering teams. - Master's degree or higher in Computer Science, Machine Learning, AI, or a related field. Compensation We are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, our total rewards package for eligible roles includes a discretionary bonus, a meaningful equity component, and comprehensive benefits. The anticipated annual base salary range for this role is: $165,000 - $310,000 USD Benefits and Perks We offer a comprehensive and competitive benefits package designed to support our employees’ health, well-being, and long-term success: - Comprehensive Health Coverage: Medical, dental, and vision coverage for employees and eligible dependents. - Meaningful Equity: RSUs that give employees a stake in the company's long-term success. - Retirement Savings: 401(k) matching (U.S.) and pension contributions (U.K.). - Flexible Time Off: Unlimited PTO, company holidays, and floating holidays to support work-life balance. - Company-Wide Winter Break: Two weeks of company closure each winter to disconnect and recharge. - Paid Parental & Family Leave: Paid leave to support you and your family through life's important moments. - Professional Development: Annual learning and development allowance to support your professional growth. - Wellness Benefits: Wellness and work-from-home stipends to support your physical and mental well-being. - Sabbatical Program: Four weeks of paid sabbatical leave after four years of service. - Flexible Work: Flexible schedules and a hybrid work model for our office-based teams. - In-Office Meals: Complimentary meals at our office hubs. Benefits may vary by location, team, and role. At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.
Research Scientist (Applied LLMs), London
Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease. The future is coming. A future enabled and enriched by the incredible power of machine learning. A future in which diseases are curtailed or cured starting with better and faster drug discovery. Come and be part of an interdisciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring and collaborative culture. The world we want tomorrow is the one we’re building today. It starts with the culture at this company. It starts with you. About Iso Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed. Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases. We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design. Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI. Research Scientist - Applied Large Language Models Your impact As a Research Scientist focused on the application of Large Language Models (LLMs), you will play an exciting role in bringing cutting edge technologies to bear across our research areas to transform the drug discovery world as we know it. Working in a highly creative, fast-paced and interdisciplinary environment, you will be partnering with leading research scientists and engineers to conceive, design, and develop cutting edge LLM applications that help to unlock new modelling capabilities and predictive power which will be critical to the organisation’s success. You will draw upon your existing deep understanding and experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems. Depending on your experience: You will both partner with teams across our existing research areas and create and lead standalone projects, bringing together a variety of disciplined scientists and engineers to pursue some of the most ambitious modelling problems through the application of LLMs - as well as providing technical mentorship and people management for others in the ML community at Isomorphic Labs. You will be instrumental in leading machine learning based research projects, building the models, and algorithms that will power our platform to drive us close to achieving our ambitious mission to one day solve all disease with the help of AI. What you will do - Contribute to core research in machine learning by pushing the boundaries of Large Language Models in their application to the world of drug discovery - Develop and refine LLM-driven approaches for multiple use cases across our research areas, using your extensive knowledge of the field to modify and apply models and explore their capabilities when applied to complex biological and medical challenges. - Identify and create novel ML techniques and the required data to train and apply models - Analyse and tune experimental results to inform future experimental directions. - Implement and scale training and inference engineering frameworks - Report and present research findings and developments clearly and efficiently, to both other ML scientists and scientists of different disciplines - Iterate collaboratively with scientists and domain experts, sharing your own domain experience - Suggest and engage in team collaborations to meet ambitious research goals Depending on your experience: - Provide technical mentorship and guidance to the ML research community, advising on projects, and shaping our research roadmap based on your deep technical expertise. - Provide developmental support to other ML research scientists. - Create, lead, and run ML research projects, fostering collaborative and diverse teams to solve high priority modelling problems. Cultivate a diverse and inclusive research culture. Skills and qualifications Essential - PhD or equivalent practical experience in a technical field - Highly specialized LLM builder with a passion for, and strong experience in, applying LLMs to novel problem spaces - Deep understanding and experience of model architectures, training methods, and techniques for application of LLMs, including deployment for bespoke use cases and applications, established experience in post-training, reasoning, test-time scaling, tool-use, alignment, agents, LLM research, as well as reinforcement learning and fine-tuning - Strong knowledge of linear algebra, calculus and statistics. - Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas. - Depending on your experience: project supervision, leadership, or management. - Strong knowledge of LLM landscape today, and likely developments for the near future - Experience working with real-world datasets. Nice to have - PhD in machine learning or computer science. - Relevant research experience to the position such as post doctoral roles, a proven track record of publications, or contributions to machine learning codebases. - A strong background in biology or medicine, enabling you to bridge the gap between advanced AI capabilities and complex scientific problems in drug discovery - Experience working in a scientific environment across disciplines (particularly biology, chemistry, physics), and with real world biological or chemical datasets and biological or chemistry software - Experience in computational chemistry, bioinformatics, or related fields would be highly beneficial, along with a track record of applying AI to real-world scientific challenges - Experience with multi-parameter optimization - Experience in any of: large scale deep learning, generative models, graph neural networks, deep learning for drug discovery, deep learning for computer vision, 3D graphics/robotics, real-world applied RL Culture and values We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. Thoughtful Thoughtful at Iso is about curiosity, creativity and care. It is about good people doing good, rigorous and future-making science every single day. Brave Brave at Iso is about fearlessness, but it’s also about initiative and integrity. The scale of the challenge demands nothing less. Determined Determined at Iso is the way we pursue our goal. It’s a confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we. Together Together at Iso is about connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere. Creating an extraordinary company We believe that to be successful we need a team with a range of skills and talents. We're building an environment where collaboration is fundamental, learning is shared and every employee feels supported and able to thrive. We value unique experiences, knowledge, backgrounds, and perspectives, and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. Hybrid working It’s hugely important for us to share knowledge and build strong relationships with each other, and we find it easier to do this if we spend time together in person. This is why we follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on which team you’re in). If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for an initial screening call. Please note that when you submit an application, your data will be processed in line with our privacy policy . >> Click to view other open roles at Isomorphic Labs
Research Engineer (LLM Performance), London
Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease. The future is coming. A future enabled and enriched by the incredible power of machine learning. A future in which diseases are curtailed or cured starting with better and faster drug discovery. Come and be part of an interdisciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring and collaborative culture. The world we want tomorrow is the one we’re building today. It starts with the culture at this company. It starts with you. About Iso Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed. Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases. We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design. Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI. Your impact This is an exciting opportunity for you to contribute to frontier research at the intersection of AI and drug design. Working in a highly creative, iterative environment, you will join the model performance and scaling team, where you will partner with scientists and engineers to scale foundational models that will transform the biopharmaceutical world as we know it. You will draw upon your existing engineering experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered model and systems performance optimizations, as well as machine learning, computational biology and chemistry problems. What you will do - Implement and optimize LLM post-training methods at scale on frontier models. - Collaborate with research teams to translate new methods into production-ready systems. - Relentlessly prioritize and execute on performance optimization opportunities. - Evaluate and deploy frameworks for supervised fine-tuning, reinforcement learning and LLM evaluation. - Diagnose and fix performance bottlenecks and communication overhead in distributed training and inference systems. - Deploy low-precision methods to balance performance with accuracy, impacting real world drug design programs. Skills and qualifications Essential: - Significant experience with large scale distributed training of LLMs. - Experience with deep learning ML frameworks (either JAX or PyTorch). - Knowledge of parallelism strategies and collective communication libraries (e.g. NCCL). - Good understanding of GPU architectures. Reasoning about performance concepts is more important than writing kernels from scratch - Excellent collaboration skills. Nice to have: - Experience with general LLM serving stacks. - Knowledge of XLA, Triton, Pallas, CUDA or similar accelerator DSLs / compilers. - Experience with optimising ML accuracy using low-precision formats. - Prior experience building, deploying and maintaining production systems on GCP. - Interest in chemistry and biology. Culture and values We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. Thoughtful Thoughtful at Iso is about curiosity, creativity and care. It is about good people doing good, rigorous and future-making science every single day. Brave Brave at Iso is about fearlessness, but it’s also about initiative and integrity. The scale of the challenge demands nothing less. Determined Determined at Iso is the way we pursue our goal. It’s a confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we. Together Together at Iso is about connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere. Creating an extraordinary company We believe that to be successful we need a team with a range of skills and talents. We're building an environment where collaboration is fundamental, learning is shared and every employee feels supported and able to thrive. We value unique experiences, knowledge, backgrounds, and perspectives, and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. Hybrid working It’s hugely important for us to share knowledge and build strong relationships with each other, and we find it easier to do this if we spend time together in person. This is why we follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on which team you’re in). If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for an initial screening call. Please note that when you submit an application, your data will be processed in line with our privacy policy . >> Click to view other open roles at Isomorphic Labs
AI Research Engineer - ML Engineering
Who we are Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards. As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously. We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications. The role At Helsing, we are pioneering the future of autonomous decision-making for defence. Our work spans the full AI landscape, including high-volume data processing, RL agent training, and large-scale foundation models. As a member of a cross-functional team, you will architect and implement the tools and platforms that enable these breakthroughs. Your focus will be on abstracting complex distributed systems to maximise training throughput and developer velocity. We are looking for engineers who can navigate the convergence of machine learning and systems engineering to build robust, scalable platforms. What you will do: - Extend our highly integrated deep learning frameworks (built on top of PyTorch), making them efficient and easy to use for a wide range of use cases. - Scale our current infrastructure and tooling stack to support faster and larger distributed training. - Design data strategy to support large scale datasets and efficient storage, ensuring GPUs stay warm. You should apply if you: - Hold an MSc or PhD in Computer Science or STEM field, with a focus on Machine Learning and Deep Learning. - Have strong software engineering skills in Python and fluency with modern DL frameworks (PyTorch/JAX/TensorFlow). You don’t just import libraries; you are comfortable writing custom layers, loss functions, and distributed training loops. - Are a clear communicator who can build from complex theoretical concepts and contribute to the company's internal engineering culture. - Have a "first-principles" mindset: you enjoy reading the latest AI optimisation blog posts and integrating them into codebases rapidly. - You have debugged production ML pipelines and can tell a good war story about finding a subtle numerical or performance issue. Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points. Nice to have - You have hands-on experience training models on large-scale GPU clusters, implementing advanced parallelism strategies, and understanding the underlying cross-node communication patterns (NCCL, MPI). - You have worked with large-scale datasets made of different modalities. Understanding the tradeoffs in locality, encoding, formats, and streaming strategies. - You are proficient with workload orchestrators like Slurm, Kubernetes, or Ray, having used them to schedule and manage complex, concurrent training jobs at scale. - You understand GPU architecture at a low level: memory hierarchies, warp execution, and what makes a GPU suited for training versus inference workloads. Join Helsing and work with world-leading experts in their fields - Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns. - The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world. - Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defence industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward. - In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts. - We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practise responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about. What we offer - Competitive salary and VSOP options - Relocation support: up to €2,500 and 4 weeks temporary accommodation - Learning: €500/£450 yearly allowance - Health & wellness: gym membership and mental health support (Nilo.health) - Social: regular company events and monthly social allowances - Enhanced parental leave: 22 weeks fully paid for primary caregivers & 6 weeks for secondary caregivers - Family support: 5 days of paid family emergency leave, 100% remote work option during pregnancy and phased return to work - A hands-on onboarding program (affectionately labelled “AI-duction”), in which you will be familiarising yourself with our tools and ML pipelines used across the company. This is your opportunity to learn our tech stack, explore the company, and learn how we get things done - all whilst working with other AI and engineering teams from day one These are the core benefits across all locations, there may be additional benefits in certain locations. Helsing is an equal opportunities employer. We are committed to equal employment opportunity regardless of race, religion, sexual orientation, age, marital status, disability or gender identity. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation. LI-FH1 Helsing's Candidate Privacy and Confidentiality Regime can be found here .
AI Research Engineer - Foundation Models
Who we are Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards. As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously. We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications. The role At Helsing, we are redefining perception by building foundational intelligence for the physical world. You will research, design, and train large-scale Foundational Models that transform complex multimodal sensor data into cutting-edge autonomous capabilities. We are seeking an individual at the intersection of AI Research and Machine Learning Engineering, with a proven track record in LLM/VLM/(multimodal) backbones. Your primary focus will be training and fine-tuning Vision-Language Models on our custom datasets to power our different products. You'll be responsible for the entire model lifecycle, from data curation and training to evaluation. You should apply if you - Hold an MSc or PhD in Machine Learning, Computer Science, or Mathematics with a focus on Deep Learning. - Possess theoretical understanding and practical experience in training Foundational Models (LLMs, VLMs, or other large-scale multimodal models) from scratch or through fine-tuning. - Have strong software engineering skills in Python and fluency with modern DL frameworks (PyTorch/JAX). You don’t just import libraries; you are comfortable writing custom layers, loss functions, and distributed training loops. - Are a clear communicator who can build from complex theoretical concepts and contribute to the company's internal research culture. - Have a "first-principles" mindset: you enjoy reading the latest ArXiv papers and implementing them into the codebase rapidly. Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points. Nice to have - Top-Tier Research Track Record: You have authored publications in top-tier conferences (NeurIPS, ICML, ICLR, CVPR, ACL) specifically regarding attention mechanisms, efficient transformers, or multimodal learning. - Experience training models on large-scale GPU clusters. - Proven experience in data curation, data cleaning, data pruning, and building robust data pipelines for large-scale datasets. Join Helsing and work with world-leading experts in their fields - Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns. - The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world. - Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defence industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward. - In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts. - We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practise responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about. What we offer - Competitive salary and VSOP options - Relocation support : up to €2,500 and 4 weeks temporary accommodation - Learning : €500/£450 yearly allowance - Health & wellness : gym membership and mental health support (Nilo.health) - Social : regular company events and monthly social allowances - Enhanced parental leave : 22 weeks fully paid for primary caregivers & 6 weeks for secondary caregivers - Family support: 5 days of paid family emergency leave, 100% remote work option during pregnancy and phased return to work These are the core benefits across all locations, there may be additional benefits in certain locations. Helsing is an equal opportunities employer. We are committed to equal employment opportunity regardless of race, religion, sexual orientation, age, marital status, disability or gender identity. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation. Helsing's Candidate Privacy and Confidentiality Regime can be found here .
AI Research Engineer - Computer Vision
Who we are Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards. As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously. We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications. The role At Helsing we deliver AI-based capabilities and the enabling foundation that allow machines to perceive and assist human decision-making. You will have the unique opportunity to shape AI capabilities in one of the most challenging sectors, where high generalisation capabilities need to be paired with hardware constraints and robustness against adversarial attacks. You will be part of a computer vision team responsible for building models for object detection, recognition, classification, and tracking. You will develop and extend state-of-the-art architectures and pipelines, design rigorous experiments, and conduct benchmarks to evaluate and improve real-world performance, including adaptation to the compute constraints and operational requirements of downstream deployments. You will collaborate across research, engineering, and product teams on high-impact projects. Typical examples of day-to-day responsibilities include, depending on the team: - Optimising models for real-time deployed performance on target hardware: selecting the best architecture or layers for throughput and latency constraints, curating and leveraging data to improve performance - Researching new model architectures, replicating and adapting SOTA detection, tracking, or classification methods to concrete use cases according to operational needs - Running multi-GPU or multi-node training jobs on large-scale sensor datasets to build powerful foundation or auto-annotation models - Interacting with product and downstream deployment teams to identify perception bottlenecks, prioritise improvements, and shape the roadmap for new capabilities You should apply if you - Hold an MSc in computer science, machine learning, robotics, or a closely related field, with experience in designing, implementing, and thoroughly evaluating advanced AI-based systems. - Have hands-on experience developing and deploying computer vision models. You have iterated on models beyond benchmarks and understand what it takes to make visual perception reliable under real-world data distributions and deployment constraints. - Are deeply familiar with modern deep learning architectures for visual perception, including convolutional and transformer-based models, and have applied techniques such as multi-task learning, domain adaptation, or model compression to practical problems. - Possess solid software engineering skills, writing clean and well-structured code in Python, and have experience deploying AI software to production including testing, QA, and monitoring. - Have excellent communication skills and the ability to report and present research findings clearly and efficiently, both internally and externally. - Are passionate about keeping up to date with current research and enjoy reimplementing and extending state-of-the-art approaches in computer vision and deep learning. Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points. Nice to have - PhD in computer vision, machine learning, robotics, or a related field, with publications in top-tier venues (e.g. CVPR, NeurIPS, ICLR, ICCV, ICRA, IROS). - Experience with object detection and classification, single and multi-object tracking, or scene understanding. - Experience designing, evaluating, and delivering end-to-end AI systems on edge devices with constrained compute resources. - Experience with simulators, emulators, or synthetic data generation pipelines. - Experience with Rust and/or C++. Join Helsing and work with world-leading experts in their fields - Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns. - The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world. - Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defence industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward. - In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts. - We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practise responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about. What we offer - Competitive salary and VSOP options - Relocation support : up to €2,500 and 4 weeks temporary accommodation - Learning : €500/£450 yearly allowance - Health & wellness : gym membership and mental health support (Nilo.health) - Social : regular company events and monthly social allowances - Enhanced parental leave : 22 weeks fully paid for primary caregivers & 6 weeks for secondary caregivers - Family support: 5 days of paid family emergency leave, 100% remote work option during pregnancy and phased return to work These are the core benefits across all locations, there may be additional benefits in certain locations. Helsing is an equal opportunities employer. We are committed to equal employment opportunity regardless of race, religion, sexual orientation, age, marital status, disability or gender identity. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation Helsing's Candidate Privacy and Confidentiality Regime can be found here .
Research Engineer, Training & Inference
ABOUT HARMONIC At Harmonic, we are building a mathematical reasoning engine that operates with absolute precision. While most AI makes maximum-likelihood guesses, Harmonic's Aristotle uses Lean 4 and reinforcement learning to verify its reasoning and results. Following our Gold Medal-level performance on the 2025 International Math Olympiad (IMO) and the successful resolution of long-standing open problems, we are proving that AI can master the most rigorous domains of human thought. Backed by some of the world’s most prominent investors, we are intentionally scaling an elite technical team. Visit our company blog https://harmonic.fun/news to learn more about what we are working on! ABOUT THE ROLE We are developing reinforcement learning systems at a scale where standard abstractions frequently fail. Unlike labs that operate primarily through high-level wrappers, we own the entirety of our RL stack. This ownership spans from low-level environment simulators and custom communication primitives to our distributed training loops and inference engines. We are seeking engineers who view existing libraries as a baseline and the hardware speed itself as the true target. You will be responsible for the architecture powering our agents, with a relentless focus on maximizing the throughput of our reinforcement learning and production workflows. KEY RESPONSIBILITIES - Total Stack Ownership: Maintain and optimize our proprietary RL training and serving infrastructure. You have the authority to refactor any layer—from the Python API down to the CUDA kernels—to achieve peak performance for foundation model workloads. - Optimized Training: maximize the throughput of our reinforcement learning system from data generation to model training with sharded multi-node training and inference algorithms. - High-Performance Serving: optimize our inference stack for high-throughput reinforcement learning and low-latency LLM production traffic. Tune the inference engine, router, and scheduler, down to custom kernels if need be. - Compute Optimization: Identify and resolve performance bottlenecks within our distributed clusters, ensuring optimal throughput and memory efficiency for multi-billion parameter models, balancing memory constraints with compute-heavy training cycles. MINIMUM QUALIFICATIONS - BS in Computer Science or a related technical field, or equivalent industry experience - 2+ years of relevant, hands-on industry experience - Proficiency in Python - Experience building or maintaining components within ML frameworks (e.g., PyTorch, JAX, or TensorFlow). - Proficiency in either: - Understanding of distributed training concepts and collective communication primitives (e.g., NCCL). OR - Practical experience deploying and profiling models on GPU-accelerated cloud infrastructure. PREFERRED QUALIFICATIONS - MS or PhD in Computer Science, Mathematics, or a related field. - 5+ years of relevant, hands-on industry experience - Proficiency in C++ - Experience writing or improving kernels (Triton, CuTeDSL, TileLang, CUDA, CUTLASS, ThunderKittens) to resolve low-level bottlenecks. - Proven success deploying performant inference at scale using open-source or custom inference engines, routers, etc. - Direct experience scaling models via FSDP, Tensor Parallelism, or related sharding techniques on multi-node GPU clusters. - Experience designing reinforcement learning systems for high-throughput training and asynchronous data sampling. WHAT WE OFFER - Unlimited PTO - 401(k) matching - 100% employer-paid health, vision, and dental benefits for employees and 50% coverage for dependents. Harmonic offers varied health coverage options to select what is best for you and your family. - Health Savings Account (HSA) available for qualifying health plans EQUAL OPPORTUNITY STATEMENT Harmonic is committed to diversity and inclusivity in the workplace. We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.
Research Engineer, Model Inference & Serving - Par...
RESEARCH ENGINEER, MODEL INFERENCE & SERVING About H: H exists to push the boundaries of superintelligence with agentic AI. By automating complex, multi-step tasks typically performed by humans, AI agents will help unlock full human potential. H is hiring the world's best AI talent, seeking those who are dedicated as much to building safely and responsibly as to advancing disruptive agentic capabilities. We promote a mindset of openness, learning, and collaboration, where everyone has something to contribute. About the Team: The Inference team builds and operates the systems that serve H's foundational models in production. We focus on multimodal inference and serving for Computer Use Agents, optimizing across both the inference engine layer (e.g., vLLM, SGLang) and the model serving layer (e.g., disaggregated inference, intelligent routing). Agentic inference brings constraints around context length, multimodality, and tool calls, which we address by co-designing with the Models team on training-time choices and with the agent teams on how models are deployed. We operate at the intersection of research and production, translating cutting-edge inference techniques into the systems that power H's next generation of agents. We are looking for strong engineers excited about inference to join the team and help shape the systems behind superintelligent AI. KEY RESPONSIBILITIES: - Build and operate the inference stack that serves H's multimodal agentic models - Improve latency, throughput, and cost of model serving across the stack - Research and implement inference techniques tailored to agent workloads - Co-design with the Models team on training-time decisions that affect inference - Collaborate with cross-functional teams to integrate inference into agentic AI products - Evaluate inference, serving, and hardware platforms, and communicate findings to stakeholders - Stay current with advancements in inference, model serving, and accelerator technology REQUIREMENTS: - Technical skills: - Strong software engineering track record - Proficient in Python and at least one systems language (Rust, C++, or Go) - Hands-on experience with deep learning frameworks (PyTorch, JAX), preferably in an industry setting - Solid distributed systems fundamentals - Experience working in a modern cloud environment and with production ML infrastructure (Kubernetes, etc.) - Working knowledge of modern ML, including transformers and multimodal architectures - Research skills: - Research engagement: an advanced degree with research output, or publications at top-tier AI or systems venues (e.g., NeurIPS, ICML, MLSys, OSDI), research internships, or substantive open-source contributions - Soft skills: - Excellent communication and presentation skills - Strong collaboration and teamwork skills - Passion for inference and AI - Preferred qualifications: - Startup experience - Hands-on experience with inference frameworks (vLLM, SGLang, TensorRT-LLM) - Writing or modifying GPU kernels (CUDA, Triton, etc.) - Edge or on-device inference experience (llama.cpp, MLX, ONNX Runtime, etc.) - Experience with quantization, speculative decoding, disaggregated inference or KV-cache compression - Experience with multimodal models and/or agentic systems LOCATION: - Paris or London. - This role is hybrid, and you are expected to be in the office 3 days a week on average. - Please expect some travel between offices on a reasonable cadence (e.g., every 4-6 weeks). WHAT WE OFFER: - Join the exciting journey of shaping the future of AI - Collaborate with a fun, dynamic and multicultural team, working alongside world-class AI talent in a highly collaborative environment - Enjoy a competitive salary - Unlock opportunities for professional growth, continuous learning, and career development If you want to change the status quo in AI, join us.
Research Engineer, Model Inference & Serving - Lon...
RESEARCH ENGINEER, MODEL INFERENCE & SERVING About H: H exists to push the boundaries of superintelligence with agentic AI. By automating complex, multi-step tasks typically performed by humans, AI agents will help unlock full human potential. H is hiring the world's best AI talent, seeking those who are dedicated as much to building safely and responsibly as to advancing disruptive agentic capabilities. We promote a mindset of openness, learning, and collaboration, where everyone has something to contribute. About the Team: The Inference team builds and operates the systems that serve H's foundational models in production. We focus on multimodal inference and serving for Computer Use Agents, optimizing across both the inference engine layer (e.g., vLLM, SGLang) and the model serving layer (e.g., disaggregated inference, intelligent routing). Agentic inference brings constraints around context length, multimodality, and tool calls, which we address by co-designing with the Models team on training-time choices and with the agent teams on how models are deployed. We operate at the intersection of research and production, translating cutting-edge inference techniques into the systems that power H's next generation of agents. We are looking for strong engineers excited about inference to join the team and help shape the systems behind superintelligent AI. KEY RESPONSIBILITIES: - Build and operate the inference stack that serves H's multimodal agentic models - Improve latency, throughput, and cost of model serving across the stack - Research and implement inference techniques tailored to agent workloads - Co-design with the Models team on training-time decisions that affect inference - Collaborate with cross-functional teams to integrate inference into agentic AI products - Evaluate inference, serving, and hardware platforms, and communicate findings to stakeholders - Stay current with advancements in inference, model serving, and accelerator technology REQUIREMENTS: - Technical skills: - Strong software engineering track record - Proficient in Python and at least one systems language (Rust, C++, or Go) - Hands-on experience with deep learning frameworks (PyTorch, JAX), preferably in an industry setting - Solid distributed systems fundamentals - Experience working in a modern cloud environment and with production ML infrastructure (Kubernetes, etc.) - Working knowledge of modern ML, including transformers and multimodal architectures - Research skills: - Research engagement: an advanced degree with research output, or publications at top-tier AI or systems venues (e.g., NeurIPS, ICML, MLSys, OSDI), research internships, or substantive open-source contributions - Soft skills: - Excellent communication and presentation skills - Strong collaboration and teamwork skills - Passion for inference and AI - Preferred qualifications: - Startup experience - Hands-on experience with inference frameworks (vLLM, SGLang, TensorRT-LLM) - Writing or modifying GPU kernels (CUDA, Triton, etc.) - Edge or on-device inference experience (llama.cpp, MLX, ONNX Runtime, etc.) - Experience with quantization, speculative decoding, disaggregated inference or KV-cache compression - Experience with multimodal models and/or agentic systems LOCATION: - Paris or London. - This role is hybrid, and you are expected to be in the office 3 days a week on average. - Please expect some travel between offices on a reasonable cadence (e.g., every 4-6 weeks). WHAT WE OFFER: - Join the exciting journey of shaping the future of AI - Collaborate with a fun, dynamic and multicultural team, working alongside world-class AI talent in a highly collaborative environment - Enjoy a competitive salary - Unlock opportunities for professional growth, continuous learning, and career development
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