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Machine Learning Scientist, BioML
Profluent is the frontier AI lab for biology. Profluent builds powerful foundation models for all of life's molecules, unlocking solutions that transform medicine, agriculture, and beyond. Founded in 2022 and headquartered in Emeryville, CA, Profluent is backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures and has raised over $150M to date. We're looking for a motivated and creative Machine Learning (ML) Scientist to drive research into models at the intersection of complex protein biology and AI. This position offers an opportunity to work at the forefront of generative modeling research across language processing, representation learning, and protein engineering. You should be a self-directed researcher who has the ability to rapidly prototype and evaluate new models and algorithms in the biomolecular domain. As an early employee, you will proactively shape the direction of our machine learning efforts and collaborate across diverse teams of computational and experimental scientists. Responsibilities - Design and develop state-of-the-art predictive and generative models incorporating domain-specific evolutionary and experimental data - Leverage massive-scale protein and nucleic acid data to train specialized models for protein understanding and design - Curate relevant datasets and design tasks for rigorous evaluation of generative models - Collaborate across the machine learning and protein design teams to adapt and apply techniques for experimental validation - Implement, analyze, and interpret multiple computational approaches and present results to colleagues in regular update meetings - Work within a collaborative, fast-paced, interdisciplinary team across biology and machine learning to help shape the scientific and strategic vision of the company Qualifications - PhD (or equivalent industry experience) in Computer Science, Machine Learning, Natural Language Processing, Applied Math, Computational Biology, Statistics, or a related field - Experience with conceiving of, implementing, and evaluating novel machine learning techniques at the intersection with biology - Publications at major machine learning conferences (NeurIPS, ICML, ICLR) or scientific journals (Nature, Science, Nature Biotech, Nature Methods, PNAS) - Experience with modern deep learning frameworks such as Pytorch or Jax Preferences - Familiarity with foundational biology of proteins and nucleic acids - Experience developing machine learning models for proteins (language models, structure prediction, design) - Experience with cloud compute platforms (GCP, AWS, Azure, OCI) - Previous experience in data extraction and curation from bioinformatics data sources - Familiarity with wet lab experimental assays and associated limitations - 3 to 5 years of industry experience What We Offer - High-growth opportunity with meaningful impact on the future of protein design - Competitive compensation package with equity participation - 401(k) with a strong employer match - Comprehensive benefits including health/dental/vision insurance - Generous PTO policy and commitment to work-life balance - Professional development opportunities in a cutting-edge field at the intersection of AI and biology Profluent Bio, Inc is an equal opportunity employer promoting diversity and inclusion in the workspace. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical conditions, veteran status, sexual orientation, gender (including gender identity and gender expression), sex (which includes pregnancy, childbirth, and breastfeeding), genetic information, taking or requesting statutorily protected leave, or any other basis protected by law. Employment Eligibility Verification Legal authorization to work in the United States is required. In compliance with federal law, all persons hired must verify their identity and work eligibility and complete the required employment verification form upon hire. Hiring Salary Range $212,000 - $339,000 USD NOTICE TO STAFFING AND SEARCH FIRMS: Profluent does not accept unsolicited candidate submissions. We ask that agencies refrain from sending resumes to our careers inbox, hiring managers, or any Profluent employee. Any resume submitted without a signed agreement and an active search request from our Talent team will be treated as the property of Profluent, and no placement fee will be owed.
Senior Machine Learning Engineer
About us Re-architecting Engineering for the Age of Intelligence PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area. Note: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals. Who We're Looking For As a Senior Machine Learning Engineer in Delivery, you are an experienced problem solver and technical leader who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, lead technical initiatives, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used. You’ve shipped ML systems end-to-end and at scale: you design, build and test reliable, scalable ML data pipelines; you know how to explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling; you select the right libraries, frameworks and tools and make pragmatic product decisions that set Delivery up for success. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products. With at least 3 years industry experience (post Masters or PhD) in a commercial, non-research environment, you're ready to not only execute but also lead and mentor others. You're truly excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers. Note: This position may require access to information protected under U.S. export control laws and regulations, including the Export Administration Regulations (EAR) and the International Traffic in Arms Regulations (ITAR). Please note that any offer for employment may be conditioned on authorization to receive software or technology controlled under these U.S. export control laws and regulations without sponsorship for an export license. This Role As a Senior MLE, you'll work closely with our Data Scientists, Simulation Engineers, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes. You'll: - Own the deployment of ML models and engineering surrogates (e.g., deep learning on CAE/CFD/FEA data, time‑series forecasting, anomaly detection, optimization & control) to customer production environments. - Communicate results and trade‑offs to senior stakeholders; steer roadmaps and influence product direction with evidence. - Lead scoping and architecture design for data/ML systems; define success metrics, delivery plans and quality bars. - Excel at building robust and scalable ML systems, training and inference pipelines and APIs, running both on cloud and on-prem environments. The tech stack you will use for this includes: Python, PyTorch, Pandas, fastAPI, Scipy, Kubeflow, among others. - Mentor and develop engineers and data scientists; provide technical direction and clear, calm decision‑making under pressure. - Travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter , where you'll collaborate closely with customers to build solutions on-site. - Own the scoping of new projects and work-streams with existing customers and taking part in bringing new customers to PhysicsX. As a senior member of the team, you’ll significantly influence our technical direction and will be involved in shaping future solutions and products, while developing your skills as a technical leader. Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey! What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our New York office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 💰 5% contribution to 401(k) - build long-term security with a strong retirement plan. 🍽️ Free team lunch 1x/week - good food, great company, and space to connect. 🏥 Private health insurance – comprehensive cover for you, offering total peace of mind. 👶 Enhanced parental leave – 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most. ☀️ 20 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. 📈 Personal development – dedicated support for learning, development, and leveling up over time. 💪 Gympass / Wellhub (subsidized) – for you and up to 3 family members, supporting both physical and mental wellbeing. 💳 Flexible Spending Account (FSA) – set aside pre-tax dollars for eligible healthcare expenses. 🔎 Watch this space, we’re continuing to build this as we grow… Salary range: $200,000 - $250,000 depending on experience Seniority will be assessed throughout our interview process We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.
Research Scientist - Large Geometry Models
About us Re-architecting Engineering for the Age of Intelligence PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area. Note: We are currently recruiting for multiple levels and positions, however please only apply for the role that best aligns with your skillset and career goals. Geometry is at the heart of how we think about engineering. You will work at the frontier of 3D geometry representation and generation, with a focus on making both of these ready for engineering. You will also work on closing the coupling between geometry and physics, towards generating shapes that are driven by physical objectives. What you will do - Own a research work-stream: set its technical direction, align priorities with internal and external stakeholders, and apply judgement and taste to drive progress. - Design and train generative models over 3D geometry — representations, latent spaces, and the objectives that decide whether generated geometry is physically usable rather than merely plausible. - Work against production numerical solvers, so model quality is measured in validated physics rather than proxy metrics. - Work with our ML engineers, simulation engineers and customers to turn engineering challenges into mathematical formulations. - Champion research directions valuable to the company, nurture more junior colleagues, and publish for both academic and non-academic audiences. What you bring to the table - PhD in CS, ML, mathematics, physics, engineering or a related field, with demonstrated expertise in generative modelling or learned representations of 3D geometry, covering one or more of: - generative models for geometry — VAEs, diffusion and flow matching, autoregressive latent models, scaled to large datasets; - geometric deep learning and 3D vision over point-cloud, mesh, voxel or implicit-field data, including sparse convolution and sparse attention; - neural fields and shape autoencoders. - Relevant experience in industry or a research group of comparable intensity, instrumental in: building models and pipelines in PyTorch/CUDA; bespoke problem settings involving geometric or 3D data; iterating on architecture and inductive bias; combining theoretical reasoning with empirical intuition; running experiment pipelines that produce comparable results. - Ability to scope and deliver projects, with strong problem-solving skills. - Enthusiasm for deep learning and probabilistic methods applied to science and engineering. - Publications at premier conferences: SIGGRAPH, SIGGRAPH Asia, CVPR, ICCV, ECCV, NeurIPS, ICML or ICLR, and relevant journals: TOG, TPAMI, JMLR. - Desirable: operator learning or probabilistic methods for PDEs; self-supervised pretraining for 3D vision and/or physics surrogacy; generative inverse design linking geometry to physics objectives.. What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 🏦 10% employer pension contribution - because investing in future matters. 🍽️ Free office lunches - to keep you energised and focused. 👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most. 🍼 YellowNest nursery scheme - to help working parents manage childcare costs. ☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. 🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind. 💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing. 👀 Eye tests - because good work depends on good health. 📈 Personal development - dedicated support for learning, development, and leveling up over time. 💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it. 🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener. 🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric. 🔎 Watch this space, we’re continuing to build this as we grow… We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.
ML Infra Engineer, Modeling
Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future. In this role you will help scale and optimize our training systems and core model code. You’ll own critical infrastructure for large-scale training, from managing GPU/TPU compute and job orchestration to building reusable and efficient JAX training pipelines. You’ll work closely with researchers and model engineers to translate ideas into experiments—and those experiments into production training runs. This is a hands-on, high-leverage role at the intersection of ML, software engineering, and scalable infrastructure. The Team The ML Infrastructure team supports and accelerates PI’s core modeling efforts by building the systems that make large-scale training reliable, reproducible, and fast. The team works closely with research, data, and platform engineers to ensure models can scale from prototype to production-grade training runs. In This Role You Will - Own training/inference infrastructure: Design, implement, and maintain systems for large-scale model training, including scheduling, job management, checkpointing, and metrics/logging. - Scale distributed training: Work with researchers to scale JAX-based training across TPU and GPU clusters with minimal friction. - Optimize performance: Profile and improve memory usage, device utilization, throughput, and distributed synchronization. - Enable rapid iteration: Build abstractions for launching, monitoring, debugging, and reproducing experiments. - Partner with researchers: Translate research needs into infra capabilities and guide best practices for training at scale. - Contribute to core training code: Evolve JAX model and training code to support new architectures, modalities, and evaluation metrics. What We Hope You’ll Bring - Strong software engineering fundamentals and experience building ML training infrastructure or internal platforms. - Hands-on large-scale training experience in JAX (preferred), PyTorch. - Familiarity with distributed training, multi-host setups, data loaders, and evaluation pipelines. - Experience managing training workloads on cloud platforms (e.g., SLURM, Kubernetes, GCP TPU/GKE, AWS). - Ability to debug and optimize performance bottlenecks across the training stack. - Strong cross-functional communication and ownership mindset. Bonus Points If You Have - Deep ML systems background (e.g., training compilers, runtime optimization, custom kernels). - Experience operating close to hardware (GPU/TPU performance tuning). - Background in robotics, multimodal models, or large-scale foundation models. - Experience designing abstractions that balance researcher flexibility with system reliability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
AI Agent Engineer
About Us Observe.AI is the AI Agents platform for customer experience, designed to help organizations deliver faster, smarter, and more efficient customer service at scale. The platform enables businesses to deploy specialized AI agents that autonomously execute work across the full CX lifecycle—from handling customer conversations to supporting frontline teams and optimizing operations. Each AI agent is purpose-built for a specific role, equipped to understand context, make decisions, take action, and continuously improve outcomes. This allows organizations to increase resolution speed, elevate service quality, and reduce operational costs while empowering your frontline team to focus on higher-value work. Built on a CX-native foundation, Observe.AI helps leading brands like DoorDash, Affordable Care, Signify Health, and Verida improve customer satisfaction, boost agent productivity, and deliver consistent, scalable performance across every customer interaction. Why Join Us We’re looking for a client-facing AI Agent Engineer to design, integrate, and deploy enterprise-grade Voice and Chat AI agents and AI Copilots. This is a hands-on, delivery-focused role centered on bringing AI solutions to life within customer environments—not building foundational AI models. You will work directly with customers to translate requirements into working solutions by configuring integrations, designing prompts and workflows, and deploying scalable AI agents. This role is ideal for someone who enjoys problem-solving across systems, working with APIs, and delivering real-world AI applications. You’ll own the end-to-end lifecycle of AI agent delivery—from initial design and integration through testing, client demos, deployment, and ongoing iteration. What You’ll Be Doing - Build & Deploy Agents: Own the implementation of AI agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. - Client Engagement: Act as the primary technical partner for customers—lead regular demos, communicate progress, gather feedback, and guide solutions from concept to production. - Systems Integration: Configure and connect systems using APIs—handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and other enterprise tools. - Telephony Integration: Set up SIP/CCaaS/PSTN routing, pass metadata, configure fallbacks, and troubleshoot call quality. - Prompt Design & Optimization: Write and refine prompts for LLM-driven agents, monitor performance, test iteratively, and ensure agents meet automation and containment targets. - Strategic Partner: Translate customer requirements into actionable solutions; work consultatively to unblock challenges in security, connectivity, or knowledge ingestion. - Cross Functional Collaboration: Collaborate with product/engineering teams to escalate platform gaps and resolve deep technical fixes and platformization, while independently driving leading client implementations. What You Bring to the Role - Bachelor’s degree in Computer Science, Engineering, or a related technical field - 1-5 years in conversational AI, solution engineering, system integration, or delivering AI/LLM-based applications in customer environments, software engineering, or system integration with hands-on delivery of AI/LLM-based solutions. - Strong ability to communicate and lead customer-facing discussions - from deep technical troubleshooting to weekly project demos. Ability to explain complex technical concepts to non-technical audiences. - Must have strong hands-on skills in prompt design, workflow building and API integration (SIP, Twilio, Amazon Connect, etc.). - Familiarity with LLMs (GPT, Claude, Gemini), vector DBs, and orchestration frameworks (LangChain, LlamaIndex, etc.). - Working knowledge of retrieval-augmented generation (RAG) concepts, implementation patterns and performance optimization. - Programming experience in Python, JavaScript, or similar for scripting and integrations - Strong problem-solving mindset: ability to find workarounds, unblock integrations, and adapt to customer-specific ecosystems. - Experience with integration tools and Integration Platform-as-a-Service (iPaaS) providers, such as n8n, Zapier, or similar platforms and proficiency in API integrations and data flow management is a plus. - Familiarity with telephony or voice systems (SIP, CCaaS, PSTN) is a plus. Our Commitment to Inclusion and Belonging Observe.AI is an Equal Employment Opportunity employer that proudly pursues and hires a diverse workforce. Observe AI does not make hiring or employment decisions on the basis of race, color, religion or religious belief, ethnic or national origin, nationality, sex, gender, gender identity, sexual orientation, disability, age, military or veteran status, or any other basis protected by applicable local, state, or federal laws or prohibited by Company policy. Observe.AI also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. We welcome all people. We celebrate diversity of all kinds and are committed to creating an inclusive culture built on a foundation of respect for all individuals. We seek to hire, develop, and retain talented people from all backgrounds. Individuals from non-traditional backgrounds, historically marginalized or underrepresented groups are strongly encouraged to apply. If you are ambitious, make an impact wherever you go, and you're ready to shape the future of Observe.AI, we encourage you to apply. For more information, visit www.observe.ai .
Solutions Architect, CSP GTM
NVIDIA’s Worldwide Field Operations (WWFO) team is looking for a Solution Architect with expertise in Generative AI, Data Science applications and Machine Learning (ML) to work with our Hyperscaler partners. In our Solutions Architecture team, we work with the most exciting platform for accelerated computing and drive the latest breakthroughs in artificial intelligence. We need individuals who can enable customer and partner productivity. Our goal is to develop long-lasting relationships with our technology partners, making NVIDIA an integral part of end-user solutions. We are looking for someone who is always thinking about artificial intelligence, someone who can maintain alignment in a fast paced and constantly evolving field. You will be working with the latest NVIDIA technologies coupled with the most advanced CSP infrastructures, changing the way people interact with technology. As a Solutions Architect, you will be the first line of technical expertise between NVIDIA, our Hyperscaler partners and our end-customers. For this role, the primary focus will be on our strategic partnership with Google (GCP). Your duties will vary from working on proof-of-concept demonstrations, to driving relationships with key technical executives and managers to evangelize accelerated computing and Generative AI. Dynamically engaging with developers, researchers, data scientists, IT managers and senior leaders is a meaningful part of the Solutions Architect role and will give you experience with a range of challenges and solutions. What You'll Be Doing: Develop and demonstrate solutions based on Hyperscalers and NVIDIA’s pioneering GenAI software and hardware technologies to developers Work directly with key customers and our Hyperscalers partners to understand their challenges and provide the best solutions based on NVIDIA products Perform in-depth analysis and optimization to ensure the best performance on GPU-accelerated systems using NVIDIA software platform. This includes support in optimization of both training and inference pipelines Partner with Engineering, Product and Sales teams to understand developer’s challenges and plan for the best suitable solutions. Enable development and growth of product features through customer feedback and proof-of-concept evaluations Build industry expertise and become a contributor in integrating NVIDIA technology into Enterprise Computing architectures What We Need to See: 5+ years of Solutions Architect/Engineering or similar experience in AI-related fields Excellent ability to listen, both verbal and written communication skills, and being comfortable with presenting technical solutions in English Expertise in deploying large-scale training and inferencing pipeline on Hyperscaler’s infrastructure, with a focus on GCP MS/PhD or equivalent in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering fields A proven track record of academic and/or industry experience in fields related to machine learning, deep learning and/or data science You are excited to work with multiple levels and teams across organizations (Engineering, Product, Sales and Marketing team) You are a self-starter with interest in growth, passion for continuous learning and sharing findings across the team Ways to Stand Out from The Crowd: Background with Hyperscaler platforms such as GCP Experience running and optimizing large scale distributed DL training Experience optimizing inference pipeline, using a range of inferencing technics (e.g., understanding of model compression techniques, model compilation or model serving) Background with working with larger transformer-based architectures Experience using DevOps technologies such as Docker, Kubernetes, Singularity, etc.
Senior Solution Architect — Sales AI Applications
NVIDIA pioneers computer graphics, gaming, AI, and accelerated computing. We are looking for a Senior Solution Architect with full-stack software engineering experience to join our team and play an important role in developing Sales AI applications. This position offers the opportunity to design, build, and evolve solutions that bring generative AI and intelligent workflows into everyday sales experiences. You will work across the application stack and collaborate with Product, AI and machine learning, Data, Security, Solution Architecture, and Engineering teams to deliver secure, reliable, and scalable solutions used globally. What you’ll be doing: Collaborate with application teams to design, develop, and maintain scalable full-stack solutions for enterprise sales workflows. Guide technical solutions across front-end, back-end, APIs, data services, integrations, and cloud infrastructure. Translate product requirements and business needs into secure, maintainable solutions and intuitive user experiences. Integrate generative AI models, AI services, APIs, retrieval systems, and agentic workflows into production applications. Design application architectures that support performance, availability, observability, security, scalability, and long-term maintainability. Lead technical design discussions, compare implementation approaches, make informed architecture decisions, and evaluate emerging technologies. Improve engineering practices for testing, code quality, continuous integration and delivery, monitoring, documentation, and production readiness. Investigate complex issues and develop solutions that improve reliability and user experience. Mentor engineers, share technical knowledge, and contribute to engineering standards and collaborative team practices. What we need to see: A bachelor’s degree or equivalent experience in Computer Science, Engineering, or a related technical field is encouraged. 10+ years of professional software engineering experience, including building and operating production applications. Experience developing full-stack applications with modern front-end, back-end, and web application technologies. Proficiency in one or more languages or frameworks, such as Python, Java, JavaScript, React, Node.js, or similar technologies. Experience designing APIs, distributed applications, data services, enterprise integrations, scalable cloud applications, databases, and messaging systems. Experience integrating AI or machine learning capabilities through APIs, models, retrieval systems, or AI services. Knowledge of software architecture, system design, security, testing, observability, and production operations. Ability to lead technical initiatives, make informed engineering decisions, communicate with technical and non-technical teams, and mentor engineers. Ways to stand out from the crowd: Experience building generative AI applications, including AI assistants, retrieval-augmented generation, agentic workflows, AI productivity tools, Python-based AI services, large language model APIs, vector databases, prompt engineering, or AI evaluation. Experience combining traditional software systems with AI models and data pipelines and progressing AI capabilities from prototype through production. Familiarity with responsible AI, data privacy, access controls, security, or enterprise AI governance. Experience with Kubernetes, containers, microservices, infrastructure as code, DevOps practices, application performance, reliability, scalability, observability, cloud cost efficiency, or globally distributed engineering 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 hardworking people in the world working for us. If you’re creative, eager to tackle meaningful business problems, and enjoy having fun, NVIDIA is the perfect company to work for.
Senior MLOps Engineer - DSX Enablement
NVIDIA is seeking a Senior MLOps Engineer to join our DSX Enablement team, collaborating closely with strategic customers to implement and enhance groundbreaking AI workloads. We partner with the world's most innovative AI companies and open-source communities to address their most challenging technical problems. What you will be doing: In this role, you will develop innovative solutions that advance AI infrastructure capabilities, advise infrastructure experts on the demands of ML workloads, help practitioners diagnose and solve full-stack AI and ML system problems, and work on a team with direct responsibility for the success of internal and external customers’ AI and ML initiatives, including LLM performance evaluation and supporting new hardware in open-source frameworks. You will: Build and deploy custom AI solutions on NeoCloud platforms and NVIDIA Cloud Partners (NCPs), including distributed training, inference optimization, and MLOps pipelines, Act as a primary technical contact for internal and external customers and partners, guiding joint engagements, ensuring the success of initiatives on DGX Cloud, and solving complex problems in production, Work closely with the teams building the infrastructure software and accelerated frameworks that support today’s most compelling AI applications, Profile and tune large-scale training and inference workloads on NCP platforms, leading efforts to reduce latency, cost, and operational risk, and Develop open-source tools and reference architectures to make it easier to build and manage machine learning and AI workloads, pipelines, and systems at scale. What we need to see: BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience. 8+ years of experience in technical roles such as data science, data engineering, or ML engineering, ideally targeting large‑scale production systems. Demonstrated AI/ML experience across multiple phases of the machine learning lifecycle, from exploratory analysis to production systems. Facility with systems topics including Linux, batch schedulers, Kubernetes, distributed filesystems, and advanced networking at datacenter scale. Solid scripting and programming skills in languages like bash and Python and solid systems programming skills in a language like C++, Go, or Rust. Experience using machine learning or deep learning frameworks for training and inference. Excellent communication and technical presentation skills, with the ability to clearly articulate architectures, trade‑offs, and recommendations to both engineering and leadership audiences. A clear record of engineering discipline and execution on interesting projects, whether you’re working alone or collaborating on a team. Ways to stand out from the crowd: Experience contributing to and working in open-source communities. Experience with the NVIDIA ecosystem, including DGX systems, CUDA, NeMo, RAPIDS, Triton, NIM, and NVIDIA networking technologies such as InfiniBand, NVLink, and RoCE. Experience and familiarity building machine learning systems in a security-critical environment and distributed training and inference frameworks. Familiarity with MLOps practices in a cloud‑native context: containerization, CI/CD pipelines, workflow automation, observability stacks, and GitOps workflows. Direct experience drawing on deep systems knowledge to diagnose and fix performance or correctness problems that span multiple layers of the application stack, like hardware, networking, accelerator, hypervisor or OS, compilers or runtimes, application code, and libraries. NVIDIA offers competitive salaries and a generous benefits package. It is recognized as one of the technology world’s most desirable employers. We have some of the most innovative and dedicated people working here. Due to rapid growth, our outstanding teams are expanding quickly. Join us to make a lasting impact on the world!
Senior Deep Learning Performance Architect
We are now seeking a Senior Deep Learning Performance Architect! NVIDIA is looking for outstanding Performance Architects with a background in performance analysis, performance modeling, and AI/deep learning to help analyze and develop the next generation of architectures that accelerate AI and high-performance computing applications. What you’ll be doing: Develop innovative architectures to extend the state of the art in deep learning performance and efficiency Analyze performance, cost and power trade-offs by developing analytical models, simulators and test suites Understand and analyze the interplay of hardware and software architectures on future algorithms, programming models and applications Develop, analyze, and harness groundbreaking Deep Learning frameworks, libraries, and compilers Actively collaborate with software, product and research teams to guide the direction of deep learning HW and SW What we need to see: MS or PhD in Computer Science, Computer Engineering, Electrical Engineering or equivalent experience 6+ years of meaningful work experience Strong background in GPU or Deep Learning ASIC architecture for training and/or inference Experience with performance modeling, architecture simulation, profiling, and analysis Solid foundation in machine learning and deep learning Strong programming skills in Python, C, C++ Ways to stand out from the crowd: Background with deep neural network training, inference and optimization in leading frameworks (e.g. Pytorch, JAX, TensorRT) Experience with relevant libraries, compilers, and languages - CUDNN, CUBLAS, CUTLASS, MLIR, Triton, CUDA, OpenCL Experience with the architecture of or workload analysis on other DL accelerators Demonstration of self-motivation, with a knack for critical thinking and thinking outside the box Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. NVIDIA's GPUs run AI algorithms, simulating human intelligence, and act as the brains of computers, robots and self-driving cars that can perceive and understand the world. Increasingly known as “the AI computing company”, NVIDIA wants you! Come, join our Deep Learning Architecture team, where you can help build real-time, efficient computing platforms driving our success in this exciting and rapidly growing field. 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 January 13, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse 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.
Research Scientist, AI Accelerator Design and VLSI
NVIDIA Research is searching for a world-class new college grad PhD researcher to drive groundbreaking research at the intersection of AI HW/SW Co-Design, AI Hardware Accelerator Architecture, IC Design Methodology, and VLSI Design. Ideal candidates will have a perspective across areas including machine learning fundamentals, quantization and numerical methods for machine learning model optimization, digital VLSI circuits for computer arithmetic, high-productivity VLSI design and verification methodologies including applications of generative AI to hardware design, AI hardware micro-architecture, and VLSI methodology and implementation. What you'll be doing: AI Accelerator Hardware: Contribute to novel research advancing the state-of-the-art in AI accelerator design. VLSI: Research creative and innovative ASIC and VLSI design techniques and/or novel digital VLSI circuits. Apply machine learning, generative AI, and innovative tools and methodologies to automated ASIC and VLSI design tool flows. AI HW/SW Co-Design: Research and develop creative and innovative numerical methods for quantization, sparsity, or tensor decomposition grounded in computer arithmetic fundamentals and digital VLSI circuits. Collaborate on the development of research prototype testchips. Collaborate with AI researchers and hardware team members in research and product teams. Publish and present your original research, speak at conferences and events What we need to see: PhD in Computer Science, Electrical/Computer Engineering, or related field (or equivalent experience) VLSI Implementation Skills: Experience in hardware design with proficiency with modern EDA tool flows. Programming & Systems Skills: Proficiency in at least two of Python, PyTorch, C++, SystemVerilog, or CUDA Publications in top circuit, architecture, and/or AI/ML venues Domain & Technical Expertise: PhD research experience in either VLSI (e.g., digital VLSI circuits and chip design methodologies), computer architecture, and/or numerical algorithms for AI model HW/SW co-design. Excellent self-motivation, a high degree of creativity, and a passion for research, collaboration skills and the ability to work effectively within a research team. Excellent written and verbal communication skills, with proven experience communicating technical work (e.g., academic presentations, poster sessions); ability to synthesize and explain complex technical concepts. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, autonomous and love a challenge, 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 168,000 USD - 264,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until February 9, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse 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 Technical Program Manager, Relational De...
NVIDIA is redefining what is possible with AI, and we are building the next generation of relational deep learning for enterprise data. Our platform learns directly from the structure and relationships inside relational databases and heterogeneous graphs, unifying GPU‑accelerated graph analytics and graph machine learning in a single stack. It powers high‑impact workloads such as fraud detection and recommender systems and helps customers unlock more value from their data. We care deeply about turning frontier AI research into reliable products that teams can trust every day, and we are excited to add a Principal Technical Program Manager who will help us do exactly that! What you will be doing: As Principal Technical Program Manager, you will lead the relational deep learning program from research to production. You will connect ML researchers, infrastructure, and platform teams so work stays aligned and delivers real impact. Deliver task‑specific models for domains such as fraud detection and recommender systems, moving from problem definition and data requirements through training, benchmarking, and hand‑off to product and customer teams. Coordinate closely with infrastructure, systems, and platform groups to align compute capacity, training and serving environments, and platform features that models depend on. Guide release management for both the platform and models, including experiment‑to‑production hand‑offs, versioning, compatibility, model cards, benchmarks, and safety and compliance approvals. Maintain the operating rhythm for the program, leading planning, reviews, risk and dependency tracking, and decision forums across research, engineering, data, product, legal, and other partners. Define and track program health metrics such as model quality, training speed, evaluation coverage, and time‑to‑release, and share clear status, risks, and decisions in executive reviews. We work as one team, we solve hard problems together, and we celebrate when complex programs ship and make a difference for customers. We believe people who enjoy building, learning, and collaborating across teams will thrive in this role. What we need to see: Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent experience. 15+ years of experience in technical program management, engineering, or data/ML delivery, including significant time in ML/AI or large‑scale data environments. Experience leading complex, multi‑stakeholder programs end to end in research and engineering organizations, with evolving requirements and clear delivery timelines. Comfort working with ML researchers, interpreting model and evaluation results, and making decisions about training pipelines, data, and infrastructure trade‑offs. Ability to build operating rhythms from scratch, influence without formal authority, and communicate clearly with both highly technical teams and senior executives. Familiarity with modern program‑management practices (for example, Agile, roadmapping, risk and dependency management) and the judgment to use them effectively in fast‑moving research settings. A hands‑on, builder mindset that includes creating automation and tooling, using AI in daily work, and applying AI to streamline program operations, status reporting, risk detection, and release workflows. Ways to stand out from the crowd: Delivering graph ML, recommender, or fraud‑detection systems into production, or shipping ML platforms and frameworks that other teams build on. Working with graph machine learning, GNNs, relational or tabular data, graph analytics libraries such as cuGraph, and the modern data stack (warehouses, feature stores, and data pipelines). Running programs that span platform and infrastructure teams and model and research teams, including GPU and compute capacity planning for training and serving. NVIDIA offers comprehensive benefits: medical, dental, and vision insurance; a 401(k) with company match; an employee stock purchase plan; flexible, generous paid time off; parental leave; and ongoing learning and development support! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 240,000 USD - 379,500 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.
PhD Research Intern, Learning Embodied Skills from...
NVIDIA is seeking outstanding Research Interns to join the Data-Driven AI for Robotics (DAIR) group. The focus is on learning embodied skills from large-scale human data. Our objective is to develop AI systems that capture, understand, and reproduce complex human motion and interaction skills across physical and digital embodiments, including humanoid robots and animated characters. Our research spans the full stack: reconstructing human motion and human-object interactions from video; generating diverse, controllable character behaviors; transferring motion across embodiments; and training physically grounded controllers for humanoid robots and interactive virtual characters. You will collaborate with a passionate and supportive research team that consistently produces influential work published at leading computer vision, machine learning, graphics, and robotics conferences. You will also have the opportunity to collaborate with world-class research and product teams across NVIDIA, following our strong “one-team” culture. What you'll be doing: Innovate and implement novel AI algorithms that transform large-scale human data into controllable motion and interaction skills across physical and digital embodiments. Develop robust, scalable training and inference pipelines for motion reconstruction, generation, retargeting, and character and robot control. Build methods that transfer human skills to humanoid robots, including whole-body loco-manipulation and dexterous manipulation. Maintain a close, collaborative relationship with your mentor(s). Publish your research findings at leading computer vision, machine learning, graphics, and robotics conferences. Partner with product teams to enable effective technology transfer of your work. Research Topics Include: Human motion and human-object interaction reconstruction, synthesis, and generation. Learning character and robot skills from video, motion-capture, and teleoperation data. Cross-embodiment motion generation, retargeting, and tracking. Whole-body humanoid control, loco-manipulation, and dexterous manipulation. Reinforcement learning and imitation learning. Differentiable physics simulation and physically grounded motion generation. World action models, vision-language-action models, video and motion foundation models, and LLM-based agents for data generation and embodied AI. What we need to see: Pursuing a PhD degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related field. Highly efficient and creative use of coding agents to accelerate research prototyping, experimentation, and development. Outstanding engineering skills in rapid prototyping and developing model-training and simulation frameworks (e.g., PyTorch, Isaac Lab, MuJoCo). Excellent skills in working with large-scale machine learning/AI systems and compute infrastructure. A promising research track record with at least one publication at leading computer vision, computer graphics or robotics conference (e.g. CVPR, ICCV, SIGGRAPH, CoRL, etc.). Experience in human motion modeling, human-object interaction, character animation, robot learning, reinforcement or imitation learning, cross-embodiment motion tracking, generative modeling, video understanding, or differentiable physics simulation is preferred. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and productive people in the world. Please join us and be at the forefront of developing the next generation of embodied AI systems, spanning humanoid robots and digital characters. Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The expected hourly rate for this position is 38 USD – 94 USD. You will also be eligible for Intern benefits . Applications for this job will be accepted at least until November 3, 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.
NIM Solution Architect
NVIDIA is leading company of AI computing. At NVIDIA, our employees are passionate about AI, HPC , VISUAL, GAMING. SA team is more focusing to bring NVIDIA new technology into difference industries. This role focuses on NVIDIA Inference Microservices (NIM), inference / RL rolloutperformance, and AI workflow enablement for LLM, VLM, and other generative AI workloads. It is a highly hands-on position at the intersection of model optimization, inference infrastructure, and customer solution delivery. What you’ll be doing: Drive the implementation, deployment, and optimization of NVIDIA Inference Microservices (NIM) solutions for enterprise and industry AI workloads. Package and serve open-source, NVIDIA, and customer-proprietary models through NIM with standardized, containerized APIs for on-premises, cloud, and hybrid environments. Optimize high-volume inference and rollout workloads for LLMs and VLMs. Evaluate and tune the NIM models. Deliver technical projects, demos and client support tasks as directed by the Solution Architecture Leadership. Provide technical support and guidance to customers, facilitating the adoption and implementation of NVIDIA technologies and products. Collaborate with cross-functional teams to enhance and expand our AI solutions portfolio. What we need to see: Master’s degree or higher in Computer Science, Machine Learning, Electrical Engineering, Mathematics, or a related technical field, or equivalent experience. 2+ years of hands-on experience in machine learning engineering, applied research, LLM/VLM inference, or RL rollout. Production-quality Python and PyTorch skills, including distributed GPU training, solution, profiling, debugging, memory optimization. Working knowledge of transformer architectures, performance optimization, rollout sampling strategies, structured generation, and model-quality evaluation. Strong written and verbal communication skills, with the ability to collaborate effectively across research, engineering, infrastructure, product, and customer-facing teams. Ways to stand out from the crowd: Publications, open-source contributions, or significant technical projects, LLM/VLM, agent systems. Experience applying programmatic verification, simulators, compilers, execution sandboxes, APIs, or external tools as reward sources for model training. agent system. Familiar with oss RL framework such as SLIME, Nemo-RL. Familiarity with enterprise AI deployment, customer adaptation, or adapting foundation models to specialized vertical domains.
ML Data Operations Lead, Dataset Release and Deliv...
NVIDIA is redefining the automotive industry through accelerated computing, artificial intelligence, simulation, and full-stack autonomous vehicle development. The pace and quality of AV development depend on delivering the right sensor, ground-truth, and derived data to machine learning teams reliably, transparently, and at scale. It's truly the data that makes the cars drive! The AV MLOps Dataset Release team transforms large-scale automotive data into versioned, trustworthy datasets used to train and evaluate machine learning models across the autonomous-driving stack. We are seeking an ML Data Operations Lead to own the customer-facing operational lifecycle of these releases. In this role, you will work at the intersection of machine learning, data engineering, infrastructure, and release operations. You will partner with ML engineers to understand their data needs, translate those needs into actionable release requirements, coordinate execution with the engineering team, and ensure every release is delivered with clear validation, documentation, and communication. This is a senior individual-contributor role. It requires sufficient technical depth to investigate problems, assess delivery risk, and challenge unclear requirements, while focusing primarily on operational ownership rather than developing the underlying data pipelines. What you'll be doing: Serve as the primary operational partner for ML engineers and other internal consumers of AV datasets. Capture and clarify dataset release requirements, including intended use cases, required signals and labels, data volumes, release cadence, delivery timelines, storage destinations, and acceptance criteria. Be responsible for the release calendar and coordinate priorities, dependencies, engineering readiness, and compute capacity across multiple concurrent dataset-release tracks. Monitor production release workflows from launch through delivery. Identify failures, stalled tasks, resource constraints, missing data, and other risks, then bring together the appropriate engineers and infrastructure owners to drive resolution. Validate release results against expected volumes, signals, versions, and quality criteria before communicating availability to customers. Maintain timely, accurate communication with customers regarding release status, risks, incidents, changing estimates, and recovery plans. Produce release notes, delivery announcements, known-issue documentation, and handoff information that enable ML teams to understand and use each dataset confidently. What we need to see: Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent experience. 6+ years of experience in ML data operations, technical service delivery, dataset operations, release operations, technical program execution, or another data-intensive operational role. Solid understanding of the machine learning data lifecycle, including data collection, curation, labeling, validation, versioning, release, storage, and consumption by training or evaluation pipelines. Ability to use SQL and data-analysis tools to investigate dataset contents, reconcile expected and delivered results, and identify quality or completeness issues. Strong customer orientation and skill in translating between ML engineers, data specialists, infrastructure teams, and other technical collaborators. Excellent written communication skills, including the ability to produce detailed requirements, release notes, status updates, incident summaries, and operating procedures. Excellent judgment when balancing customer timelines, engineering capacity, system reliability, data quality, and competing release priorities. Proven track record of influencing without direct authority and driving work to completion across a highly matrixed organization. Comfort operating in a fast-moving environment where requirements, data availability, and technical constraints may change quickly. Ways to stand out from the crowd: Experience operating large-scale dataset generation, materialization, validation, or delivery workflows, especially for autonomous-driving, ADAS, robotics, or computer-vision systems. Familiarity with automotive sensor and ground-truth data, including camera, lidar, radar, mapping, calibration, or multimodal datasets. Hands-on experience with Python, notebooks, Databricks, dashboards, or lightweight automation used to investigate data and improve operational workflows. Experience defining service-level objectives, operational metrics, alerting, incident-management practices, and root-cause corrective actions. A track record of converting frequently repeated customer requests or operational problems into standardized, automated, and scalable services. NVIDIA brings together some of the most skilled and creative people in technology to solve problems that were once considered impossible. You will have the opportunity to work with teams advancing autonomous vehicles, artificial intelligence, accelerated computing, and large-scale data systems while directly improving the speed and reliability of machine learning development. #AutonomousVehicles Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 258,750 USD for Level 4, and 200,000 USD - 322,000 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 14, 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.
Director, Perception - Autonomous Vehicles
Achieving what has never been accomplished requires foresight, creativity, and the finest talent worldwide! We are looking for a driven Director of Perception to help lead our AI model evolution. In this critical leadership role, you will drive the development of state-of-the-art deep learning models that enable our vehicles to see, understand, and navigate complex environments. You will lead teams pushing the boundaries of modern network architectures to deliver a robust, real-time 3D world model. If you are a relentless problem-solver who thrives on building world-class teams and shipping production AI, we want to hear from you. What you'll be doing: Lead and Inspire: Manage, mentor, and scale a high-performing organization of engineering managers, applied researchers, and software engineers dedicated to autonomous vehicle perception. Drive the Architecture: Guide the strategic direction, design, and execution of modern network architectures (e.g., Transformers, BEV, Occupancy Networks, Vision-Language-Action models) for multi-sensor fusion (camera, LiDAR, radar) and real-time 3D world reconstruction. End-to-End Ownership: Oversee the entire machine learning lifecycle—from active learning, data mining, and synthetic data generation to model training, model performance acceleration, and deployment on NVIDIA DRIVE platforms (Orin/Thor). Cross-Functional Teamwork: Partner closely with leaders across Planning and Control, Mapping, Hardware, and Safety teams to ensure seamless integration of the perception stack and alignment on AV system-level metrics. Cultivate Excellence: Foster a culture of radical ownership, high-velocity execution, and continuous innovation. Ensure all algorithms and production code adhere to strict automotive quality and safety standards (e.g., ISO 26262). What we need to see: Ph.D. or master's degree in computer science, Robotics, Artificial Intelligence, visual computing, or a related field (or equivalent experience). 10+ overall years of industry experience in deep learning, computer vision, or autonomous robotics, with at least 5+ years in a senior leadership role managing large teams or multiple layers of management. Deep, hands-on theoretical and practical expertise with modern network architectures (Transformers, CNNs, Foundation Models) and complex multi-sensor fusion paradigms. A proven track record of successfully deploying production-grade, safety-critical machine learning models to edge/embedded computing platforms. Strong foundational knowledge in Python, C++, and deep learning frameworks (PyTorch, JAX, or TensorFlow). Exceptional communication and leadership skills. You must be able to distill complex technical uncertainty into clear, actionable strategies for both your team and executive leadership. A highly motivated, entrepreneurial mindset. You possess a relentless drive to solve the hardest problems in the AV space and the energy to lead a team through the challenges of shipping bleeding-edge technology. Ways to stand out from the crowd: Demonstrated success in shipping autonomous vehicle software to mass-production. A recognized presence in the global AI/CV community, evidenced by top-tier publications (CVPR, ICCV, NeurIPS) or significant open-source contributions. Hands-on experience with next-generation paradigms, such as end-to-end autonomous driving architectures, Large Vision Models (LVMs), or sim-to-real transfer techniques. Deep understanding of hardware-software co-design, specifically optimizing modern networks for NVIDIA GPU architectures. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and dedicated people in the world working here. If you're creative and autonomous, we want to hear from you! #AutonomousVehicles Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 320,000 USD - 488,750 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 14, 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.
Artificial Intelligence Research Intern - Deep Lea...
NVIDIA is searching for research interns to join our team in Taipei. We are passionate about research that pushes boundaries but also has impact in the real world. Specific areas we are excited by include but are not limited to VLA, efficient DL, 3D vision, AI safety, and speech LLMs. You will work within an amazing and collaborative research organization that consistently publishes at the top venues across computer science. You have the opportunity to join and shape a new research team, where your contributions can create real impact on our products. What you will be doing: Research, design and implement novel AI and deep learning methods. Publish original research. Collaborate with other team members and teams. Speak at conferences and events. Collaborate with external researchers. What we need to see: Ph.D. students in Computer Science/Engineering, Electrical Engineering, etc. Excellent knowledge of theory and practice of computer vision, natural language processing, speech/audio processing, and deep learning methods. Excellent programming skills in some rapid prototyping environment such as Python; C++ and parallel programming (e.g., CUDA) is a plus. Knowledge of common machine learning frameworks, such as PyTorch. Outstanding research track record. Very good publication record. Excellent communication skills. NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and productive people in the world working for us. If you're creative and autonomous, we want to hear from you! We are an AA/EEO/Veterans/Disabled employer. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Software Engineer, ML Infrastructure, Optimization
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 Autonomy ML Infrastructure team is responsible for building & improving the core infrastructure for autonomy teams at Nuro. In this role, you will work closely with teams across Nuro, to design, build and deploy core infrastructure components in machine learning model life cycle, to push the autonomous future forward. You will have an opportunity to work across the full stack of machine learning solutions - from designing robust and scalable model pipelines to building to deploying the optimized models on Nuro’s fleet of self-driving robots! About the Work - Optimize Nuro’s autonomy stack with cutting-edge optimization techniques like quantization, low precision inference, and model pruning. - Work with autonomy engineers to optimize, validate, and deploy large language models. - Develop and maintain a world-class model compiler framework, FTL . - Write robust, high-quality software to increase our confidence in our vehicle’s ability to navigate safely on-road. - Collaborate closely with machine learning domain experts and engineers across behavior, perception and mapping to design and implement end-to-end learned ML solutions. About You - 2+ years of relevant experience in ML optimization infrastructure. - Experience with ML optimization techniques such as quantization and pruning, and ML compilers. - Experience maintaining, profiling, and optimizing GPU ML compilers & runtimes. - Proficient in Python and working experience with C++ and CUDA. - Working experience deep learning frameworks (like PyTorch, Jax, Tensorflow, Keras). - Proficient in Python and working experience with C++. - You are passionate about accelerating the benefits of robotics for everyday life. At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $160,360 and $240,540 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
Software Engineer, AI Platform - Intern
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 As a software engineering intern, you will work closely with leading experts in the field of machine learning, robotics, and software. Depending on your skill sets and areas of interest, you will work on some or all of the following: Data Platform, Onboard Systems, ML Infrastructure, Simulation, or Technical Infrastructure teams. About the Work Depending on your skill set and areas of interest you will work on some or all of the following: - Data Platform: The Data Platform serves as a comprehensive management system for Nuro AI Driver's data, labels, and metrics, facilitating seamless access functionality. The team focuses on data annotation across various domains, including 2D/3D perception, mapping, behavior trajectory, and language/text. It also handles data ingestion and mining, employing methods such as heuristics and embedding search. Additionally, the platform supports the autonomy evaluation infrastructure by providing detailed introspection. - Onboard Systems: Our onboard system team’s software engineers provide a reliable and high-performance platform that allows our autonomy teams to integrate their autonomy software and algorithms that work across various self-driving platforms. This work requires close collaboration with our software teams, hardware teams, and systems/safety team to make sure new software and hardware work together safely and reliably and resolve onboard error and performance problems. - ML Infrastructure: The ML Infra team is the accelerator to our ML-first autonomy strategy. This team provides solutions to empower machine learning development in Nuro and optimize on-cloud training and onboard inference. Our solutions include a distributed training platform, ML compiler, model components libraries, e.t.c. The team provides opportunities for infra engineers to work fully embedded in ML teams to build cutting edge deep learning technologies. - Simulation: The Simulation team builds the simulator that allows us to develop and test our autonomous driving technology in a virtual setting. We work on the core simulator and simulation frameworks, sensor simulation, scenario generation, and solutions that combine real-world data with synthetic techniques to push the boundaries of what can be simulated, collaborating closely with teams across Autonomy and AI Platform to allow us to simulate realistically and reliably at scale. - Technical Infrastructure: this group owns few fundamental services for entire engineering organizations: generic compute platform to host mission-critical workflows such as data processing and simulation, storage management service which manages hundreds of PB of data, cloud infrastructure serves as IaaC which provisions and maintains all cloud resources, engineering productivity provides tools such as build and CI/CD to make engineering work more efficient. About You You have deep expertise and prior experience in some or many of the following areas: - You are a current BS or MS candidate in Computer Science, Electrical Engineering, Robotics, or a related field graduating in December 2026 or later - You have experience in one or more of the following areas: backend API design, applications development, large-scale distributed systems; data storage and processing systems; advanced algorithms using C++ and Python; machine learning, multithreading; x86 architecture; and software performance tuning and optimization, robotics software frameworks, different compute modalities (CPU, GPU, FPGA) etc. - You have strong problem solving and programming skills. 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.
Machine Learning Research Scientist, Behavior Plan...
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 learned behavior team is to use advanced machine learning techniques to accelerate software progress. In this role, you will work closely with the software vertical teams to understand their pain points and explore novel and advanced machine learning methods to solve practical real-world challenging problems. To name a few, using self-supervised learning to learn robust representations, exploring techniques for out-of-distribution detection to solve long tail problems, adjusting reinforcement learning techniques for motion planning, working on trajectory prediction and motion planning, investigating the robustness of models to mitigate uncertainties, or trying to build an end-to-end driving system. If you love solving challenging new problems with a mindset of deriving practical solutions to eventually be used on the vehicle, come join us! About the Work - Work on scalable machine learning based planning and prediction systems to generate safe and feasible trajectories for autonomous driving. - Research generative sequence modeling and sequential decision making. Research backgrounds we are looking for but not limited to are: - Nice to have: Embodied AI for robotics, Causal reasoning, Model interpretability and explainability, Joint prediction and planning. - Mitigate uncertainties accumulated through interconnected autonomy components. - Collaborate across autonomy teams while developing holistic solutions to top autonomy challenges. Understand issues, propose ideas, prioritize work and develop solutions to solve them. - Derive practical solutions and deploy them on real-world vehicles. About You You have deep expertise and prior experience in some or many of the following areas: - 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 - You have subject matter expertise and research in one or more of the following areas: sequential decision making, Imitation Learning, Deep Reinforcement Learning, generative modeling, large models (pretraining/finetuning), machine learning for robotics. - You have strong problem solving and programming skills in Python (required) and C++ (beneficial) - Strong culture fit and good team player - Demonstrated research publications in top conferences (e.g. NeurIPS, ICLR, ICML, CVPR, RSS, CoRL, ICRA) 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. At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected pay range is between $160,360 and $240,540/ year 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.
Director of AI Data Operations
Who We Are Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale, empowering a safer, richer, and more connected future. About the Role Data is the foundation of autonomous driving. Every improvement in Nuro’s autonomy stack depends on identifying the right real-world examples, transforming them into high-quality training and evaluation datasets, and rigorously validating that each software release is ready for the road. As Director of AI Data Operations, you will lead the organization responsible for this critical development loop—from producing the data that trains and evaluates Nuro’s AI models to identifying autonomy regressions and building confidence in every software release. Your work will directly influence how quickly our models learn, how effectively engineering teams resolve performance gaps, and how safely new capabilities reach the vehicle. Sitting at the center of Nuro’s autonomy organization, your team will work closely with machine learning, autonomy, simulation, onboard systems, data science, and product teams. You will also help Nuro take advantage of emerging technologies, including multimodal foundation models, LLMs to improve how training data is created and how system performance is evaluated. We are looking for a proactive, technically fluent operational leader who can identify gaps across the autonomy development lifecycle and build the teams, systems, and partnerships needed to close them. You will define the long-term vision for software operations while developing an organization equally capable of navigating ambiguity, executing disciplined processes, and staying aligned with Nuro’s broader technical roadmap. About the Work - Direct the strategy and execution of the Software Operation organization, including providing high quality data for AI models, conducting triage and root cause analysis to support autonomy and system teams. - Own data labeling vendors as the extended software operation organization. Be responsible for quality, throughput SLAs, budgets, pricing negotiation. - Manage embedded domain specialists doing deep-dive work on autonomy stack modules, and partner closely with engineering and data science to build out scenario and evaluation datasets. - Set the operational KPIs, throughput metrics, and quality bar across all pipelines, and act as the main connective tissue between Software Operations, Engineering, and Product. - Run a multi-pillar organization efficiently, evolving the organization structure to align with the business needs. About You - 10+ years of operational leadership experience, with 5+ years in AV, robotics, or complex ML operations including budget or P&L ownership - Proven track record managing large-scale onshore and offshore vendor operations (labeling/annotation), both operationally and commercially - Strong working knowledge of AI systems and cross-functional collaboration with engineering and product teams - Strong analytical skills to identify root causes of KPI gaps and drive execution to resolve them - Demonstrated ability to build and scale operational organizations in fast-paced, ambiguous environments Bonus Points - Background in autonomous vehicles or physical AI - Experience with LLM or GenAI labeling pipelines At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $193,000 and $291,000 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, Algorithms
NORMAL COMPUTING | BUILD WITH US Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), London, Copenhagen, and Seoul. THE ROLE You will develop the computational methods that make AI inference run efficiently on Normal's thermodynamic hardware. The core challenge is not adapting standard GPU kernels to a new chip. It is rethinking how operations like attention, memory access, and long-context decoding behave when the underlying substrate uses stochastic analog computation in memory rather than conventional digital logic. Normal's ASICs run the heaviest operations of large model inference inside memory itself. Your job is to develop the algorithms that exploit this natively: understand what transformer and diffusion workloads are well-suited to stochastic analog execution, design numerical methods that map onto the hardware's physical dynamics, and validate them against real silicon or high-fidelity simulation. This is a co-design role. The hardware and the algorithms are developed in parallel, which means you will influence architectural decisions, not just implement against a fixed specification. The strongest candidates have a deep understanding of both large model inference and the mathematics of stochastic systems, and have built systems that run on real hardware, not just in theory. WHAT YOU WILL OWN - Algorithm Development: Develop algorithms for transformer inference workloads running on stochastic analog processing-with-memory hardware. - Software/Hardware Co-Design: Work directly with hardware and architecture teams to shape what the chip can and should compute natively. - Numerical Methods: Design numerical methods that exploit thermal noise and analog dynamics rather than working around them. - Evaluation & Benchmarks: Build evaluation frameworks and benchmarks that characterize algorithm behavior on real hardware or simulation. - Workload Translation: Translate insights about model workloads into constraints and opportunities for hardware design. - Rapid Prototyping: Prototype and iterate rapidly as hardware evolves from simulation to silicon. - Optimizing Performance: at the gate level and the algorithmic level. and algorithms (expand/review), reinforcement learning tools. WHAT MAKES YOU A GREAT FIT - Deep understanding of large model inference: attention mechanisms, KV cache, long-context decoding, memory bandwidth constraints - Experience with inference optimization: quantization, sparsity, kernel fusion, or memory-efficient attention - Familiarity with stochastic systems, probabilistic methods, numerical analysis, or analog computation - Experience implementing algorithms close to hardware, not just in high-level frameworks - Comfort reasoning from first principles about what a novel substrate can do efficiently - Track record of taking ideas from theory to working implementation on real hardware - Strong programming skills in Python and at least one systems language - Collaborative instinct and ability to work across hardware, architecture, and software teams BONUS POINTS - PhD in machine learning, applied mathematics, physics, electrical engineering, or a related field - Exposure to analog or mixed-signal systems, in-memory compute, or non-von-Neumann architectures - Experience working on hardware that did not yet exist when you joined - Publications or open-source work in efficient inference, stochastic algorithms, or novel computing Equal Employment Opportunity Statement Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status. Accessibility Accommodations Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com. Privacy Notice By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Applicant Privacy Notice https://normalcomputing.com/applicant-privacy-notice.
AI Research Resident
NORMAL COMPUTING | BUILD WITH US Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), London, Copenhagen, and Seoul. THE RESIDENCY PROGRAM The AI Research Residency is Normal Computing's flagship program for exceptional researchers and engineers who want to work at the frontier of agentic AI. Residents join a small, hand-picked cohort with a dedicated research mentor, direct access to the team building our agentic code generation platform, and a clear arc from onboarding through publication. Every residency is built around two milestones that mark you as part of something bigger than a single project: a research paper co-authored with our team, and a company-wide research colloquium where you present your findings to the full Normal Computing organization. You'll leave with a body of published, presented work, and a standing as one of the earliest residents to help define what this program becomes. YOUR NORMAL EXPERIENCE - You'll spend your residency embedded with the team advancing agentic LLMs and reinforcement learning at Normal Computing — designing experiments, building agents, and creating the evaluations that tell us whether any of it actually works. - This is a hands-on research residency: you'll take ownership of a real technical problem in agentic code generation and tool use, work alongside the researchers and engineers building our platform, and be expected to contribute ideas, not just execute someone else's. - Your job is to help turn research into production-quality research code that a team can build on, and, where the work is ready, into customer-facing improvements. Cross-functional collaboration with our hardware team will be encouraged, for example by bringing novel AI tools to enable our hardware efforts via recursive self improvement. Outside-of-the-box thinking is encouraged. - You'll get direct exposure to the pace, ambiguity, and speed of decision-making that comes with working at a fast-moving, well-funded startup — where the distance between an idea on a whiteboard and a feature in front of a customer is measured in weeks, not years. WHAT YOU'LL DO - Develop multi-agent and RL strategies. Help build multi-agent and reinforcement learning strategies for agentic code generation and tool use, and turn them into research prototypes integrated with our code generation platform. - Build evaluation suites and dashboards. Help build comprehensive evaluation suites — task specifications, benchmarks, and performance dashboards — that tell us honestly how agentic and sequential-decision systems are actually performing. - Acquire and curate datasets. Source and curate datasets from technical documents and other materials, and generate synthetic data where real data is scarce or the task calls for it. - Drive a research question of your own. Partner with researchers and engineers to scope, run, and iterate on an original technical investigation, with rigorous experimental analysis and documentation along the way. - Co-author a paper. Work with the team to write up your findings for submission to a relevant venue, with mentorship on framing, experiments, and technical writing along the way — one of the two milestones every resident builds toward. - Present at the residency colloquium. Share your work and thinking with the broader Normal Computing research community at the program's capstone event, and get real-time feedback from people building this technology every day. - Experience startup pace firsthand. Work directly with founders, senior researchers, and engineers in a lean, fast-moving environment where priorities shift quickly and your work has an outsized, visible impact. WHAT WOULD MAKE YOU A GREAT FIT - Currently pursuing or recently completed a graduate degree (MS or PhD, or equivalent research experience) in computer science, AI, machine learning, or a related field. Publications are a strong plus, but not required at the resident level — a solid research track record is what matters. - Strong Python skills and proficiency with modern ML frameworks (PyTorch preferred). - Familiarity with agentic LLM concepts — multi-agent systems, tool use, reinforcement learning variants, constrained decoding, program synthesis — and a genuine interest in keeping current with the field. Deep production experience isn't expected at the resident level, but the intuition should feel familiar. - Some experience (course projects, research, or otherwise) turning a research idea into working, reasonably reproducible code — you care about whether results replicate, not just whether a demo works once. - Comfort with, or eagerness to learn, the practical side of research: acquiring and curating datasets, thinking through licensing and provenance, and building evaluation frameworks for sequential or agentic tasks. - Bonus: prior work in program synthesis, code generation, constrained decoding, or offline RL; open-source contributions to frameworks like CleanRL, LangGraph, or Transformers; or exposure to the semiconductor domain. - Strong written and verbal communication skills; prior experience writing up research (papers, theses, technical reports) is a plus, as is any experience presenting technical work to a live audience. - A bias toward ownership and self-direction — you're energized, not overwhelmed, by the ambiguity and speed of a small, fast-moving startup. Equal Employment Opportunity Statement Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status. Accessibility Accommodations Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com. Privacy Notice By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Applicant Privacy Notice https://normalcomputing.com/applicant-privacy-notice.
Machine Learning Engineer, Frontier Data Products
ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. About the Role: Frontier AI companies are increasingly bottlenecked on expert judgment — capturing it reliably, validating it at scale, and turning it into durable model behavior. This role sits at the center of that problem. You'll build the ML systems that power Mercor's Frontier Data Products: the infrastructure that scores, validates, and improves complex work products where correctness is rarely binary and labels are often noisy, delayed, or disputed. A single job can stay live for days, interleaving model inference, automated checks, expert review, disagreement resolution, and feedback loops. Your work determines how models reason over ambiguous inputs, when they should defer to humans, how quality is measured, and how feedback compounds into better systems over time. This is applied ML product engineering under real production constraints — incomplete ground truth, shifting requirements, latency and cost tradeoffs, and workflows where a silent model failure corrupts the final output. It is not an offline benchmarks role. What You'll Do • Build ML systems that score, validate, and improve complex work products where correctness is nuanced and labels are imperfect. • Design evaluation frameworks for ambiguous tasks where ground truth is partial, delayed, or disputed. • Build feedback loops that turn review, disagreement, correction, and adjudication into measurable model and system improvements. • Own production ML behavior end-to-end: precision/recall tradeoffs, regression detection, drift, latency, cost, and explainability. • Improve model quality using the right tool for the job — prompting, fine-tuning, retrieval, active learning, heuristics, and error analysis. • Partner with backend engineers to integrate inference into durable, long-running workflows without sacrificing debuggability or human oversight. What Makes This Role Different • The architecture is not set — early engineers will define how quality is measured, how models and humans interact, where automation is trusted, and how the system compounds over time. • The feedback loop is short: shipping a model behavior change directly and visibly affects what customers receive. • You're working on a strategically central product area at Mercor at a moment when frontier AI companies have no good solution to the problem you're solving. Day-to-Day • Moving fast on a young, high-ownership codebase where your decisions have long-term architectural weight. • Operating across models, data, backend systems, and product surfaces — context switching is the default, not the exception. • Debugging production ML failures in live, long-running workflows where silent errors matter. • Working closely with backend engineers on a stack of Python, Temporal, Postgres, AWS, and LiteLLM. • Balancing automation confidence with human review — knowing when to defer is as important as knowing when to ship. What We're Looking For • Track record of shipping ML systems that improved a real product, workflow, or business metric. • Strong instincts for model quality, evaluation design, error analysis, and production failure modes. • Comfort operating in ambiguous problem spaces where labels are imperfect and correctness evolves. • Sound judgment about when to reach for prompting, fine-tuning, heuristics, retrieval, human review, or a simpler product constraint. • Solid engineering fundamentals across the full ML stack — not just modeling. • Familiarity with LLM applications, model-assisted workflows, evaluation frameworks, or human-in-the-loop ML is a strong plus. You're likely someone who: • Defaults to simple, inspectable ML systems that improve quickly and fail in understandable ways — not the most impressive architecture. • Gets uncomfortable when a model ships without a clear evaluation story. • Can hold ambiguity without paralysis and make reasonable bets with incomplete information. • Cares about the real-world output of the system, not just the benchmark. Benefits • Bi-annual performance bonus structure. • Generous equity grant vested over 4 years. • Up to $15k Relocation bonus. • $10K housing bonus (if you live within 0.5 miles of our office). • $1.5K monthly stipend for meals. • Free Equinox membership. • $200 monthly laundry reimbursement. • $200 monthly personal wellness reimbursement. • Health, Dental, Vision insurance.
Data Scientist, Agent
TL;DR — You own how we measure and improve Lovable's AI agent. You build the eval systems and experiments that tell us whether a change makes the agent better or worse, and you turn agent telemetry into the fixes that raise success rates and cut errors. At Lovable, data scientists are not isolated model-builders; they sit close to the product, experimenting continuously with how intelligence changes user behavior and product dynamics. 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 - A data scientist who wants to make an AI agent measurably better, not just report on it. You own agent quality metrics and drive them up. - Experience or strong interest in LLM evaluation and observability: building evals, scoring outputs, tracing agent behavior, and catching regressions. - Strong SQL and Python, applied statistics, and experimentation. Comfortable designing A/B tests for agent changes where outcomes are noisy. - You build systems and agents that produce this insight continuously, rather than one-off analyses. - Instinct for what "good" looks like in agent behavior (success, error rates, task completion) and how to measure it when there is no clean answer key. - Entrepreneurial. Thrives in ambiguity, and works closely with the engineers building the agent. WHAT YOU'LL DO - Define and own the metrics for agent quality: success, completion, error rates, and the behaviors that drive them. - Build the eval systems and experiment framework that decide whether an agent change ships, like an A/B-tested rollout that catches a change increasing errors before it reaches everyone. - Turn agent traces and telemetry into concrete fixes, working directly with the agent engineering team. - Build the tooling and agents that produce these evaluations continuously as the agent evolves. - Set the bar for how we judge agent behavior where there is no answer key to check against. OUR TECH STACK We're building with tools that both humans and AI love: - Languages: SQL and Python - LLM evaluation & observability: Braintrust, OTEL tracing, many LLM providers - Warehouse & events: BigQuery, PubSub - Analytics & product: Hex, Lovable Apps - Experimentation: A/B and growth testing - Cloud: GCP And always on the lookout for what's next. HOW WE HIRE 1. Fill in a short form and jump on an intro call with our recruiting team 2. A call with the hiring manager 3. A take-home case study 4. A Most Impressive Project session 5. Cross-functional interviews with the people you'd work with 6. A final conversation with leadership 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.
Member of Technical Staff - Applied ML, RecSys
ABOUT LIQUID AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. THE OPPORTUNITY This is a rare chance to apply frontier sequential recommendation architectures to real enterprise problems at scale. You will own applied ML work end-to-end for recommendation system workloads, adapting Liquid Foundation Models for customers who need personalization and ranking capabilities that run efficiently under production constraints. Unlike most recommendation roles that are siloed into a single product surface, this role gives you full ownership over how large-scale recommendation models are adapted, evaluated, and deployed for enterprise customers. Between engagements, you will build reusable applied tooling and workflows that accelerate future delivery. If you care about data quality at scale, user behavior modeling, and making recommendation systems actually work in enterprise production environments, this is the role. WHAT WE’RE LOOKING FOR We need someone who: - Takes ownership: Owns customer recommendation system engagements end-to-end, from requirements through delivery and evaluation. - Thinks at scale: Can reason about user interaction data, sequential modeling, feature engineering, and evaluation across large-scale production systems. - Is pragmatic: Optimizes for measurable customer outcomes (engagement, conversion, revenue lift) over theoretical novelty. - Communicates clearly: Can translate between customer business metrics and internal technical decisions, and push back when needed. THE WORK - Act as the technical owner for enterprise customer engagements involving recommendation and ranking workloads - Translate customer requirements into concrete specifications for recommendation models - Design and execute data pipelines for user interaction data, feature engineering, and training data curation at scale - Fine-tune and adapt large-scale sequential recommendation models (e.g., HSTU-style architectures) for customer-specific use cases - Design task-specific evaluations for recommendation model performance (ranking quality, latency, throughput) and interpret results - Build reusable applied tooling and workflows that accelerate future customer engagements DESIRED EXPERIENCE Must-have: - Hands-on experience building or fine-tuning recommendation models at scale (not just off-the-shelf collaborative filtering) - Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systems - Strong intuition for data quality and evaluation design in recommendation contexts (offline metrics, A/B testing, business metric alignment) - Experience with large-scale data pipelines for user interaction data and feature engineering - Proficiency in Python and PyTorch with autonomous coding and debugging ability Nice-to-have: - Experience with transformer-based recommendation architectures (HSTU, SASRec, BERT4Rec, or similar) - Experience delivering recommendation systems to external customers with measurable business outcomes - Familiarity with serving recommendation models under latency and throughput constraints WHAT SUCCESS LOOKS LIKE (YEAR ONE) - Independently owns and delivers enterprise recommendation system engagements with minimal oversight - Is trusted by customers as the technical owner, demonstrating strong judgment on the tradeoffs between model quality, latency, and business impact - Has built reusable applied workflows or tooling that accelerate future customer engagements WHAT WE OFFER - Real ML work: You will build and adapt large-scale recommendation models for enterprise customers, working with frontier architectures like HSTU under real production constraints. - Compensation: Competitive base salary with equity in a unicorn-stage company - Health: We pay 100% of medical, dental, and vision premiums for employees and dependents - Financial: 401(k) matching up to 4% of base pay - Time Off: Unlimited PTO plus company-wide Refill Days throughout the year
Staff/Principal DevOps Engineer, AI Inference
Your Impact at LILA The Staff/Principal DevOps Engineer - AI Inference will drive the design, implementation, and optimization of infrastructure purpose-built for serving machine learning models at scale. This role bridges platform engineering, site reliability, and ML infrastructure, building the systems that power low-latency, high-throughput inference across GPU clusters and cloud accelerators. You will collaborate with ML engineers, research scientists, and software engineers to build inference platforms that serve models reliably to production users while maximizing compute efficiency. What You'll Be Building - GPU/accelerator infrastructure on Kubernetes: scheduling, resource isolation, multi-tenant GPU sharing, device plugins, and topology-aware placement for inference workloads - Model serving platforms using frameworks such as vLLM, Triton Inference Server, TGI, or custom serving stacks with optimized batching, caching, and request routing - Intelligent request routing and load balancing across heterogeneous accelerator fleets (NVIDIA GPUs, AWS Inferentia/Trainium) to maximize utilization and minimize latency - Autoscaling systems that dynamically match inference compute supply with demand across production, research, and experimental workloads - Production-grade deployment pipelines for ML models: canary rollouts, A/B testing, model versioning, and safe rollback across multi-region deployments - Infrastructure-as-code with Terraform and Helm for GPU-accelerated EKS clusters, including node pools, spot/on-demand strategies, and accelerator-specific networking - Observability and performance optimization: GPU utilization monitoring, inference latency profiling, token throughput dashboards, and SLO/SLI tracking for model endpoints - CI/CD pipelines for model artifacts: container image builds with CUDA/driver dependencies, model registry integration, and automated inference benchmarking in CI - AWS cloud infrastructure for ML: EKS with GPU node groups, EC2 accelerated instances (P4/P5, Inf2, Trn1), S3 model storage, EFA/high-bandwidth networking, and IAM least privilege - Cost optimization and capacity planning: right-sizing accelerator instances, spot instance strategies for inference, and fleet-wide efficiency reporting What You'll Need to Succeed - Expertise in DevOps, SRE, or Platform Engineering with significant experience operating GPU/accelerator infrastructure at scale - Deep experience with Kubernetes for ML workloads: GPU scheduling, resource quotas, node affinity, and accelerator device management - Strong proficiency deploying to AWS using infrastructure-as-code (Terraform, Helm) with hands-on experience managing GPU-based compute (EKS, EC2 P-series/Inf/Trn instances) - Experience with model serving infrastructure: inference servers, request batching, KV-cache optimization, or LLM serving frameworks - Strong understanding of networking for distributed inference: high-bandwidth interconnects, NCCL, VPC/PrivateLink, and load balancing at L4/L7 - Strong proficiency in Python for automation, tooling, and integration with ML frameworks Bonus Points For - Experience with LLM inference optimization: continuous batching, speculative decoding, quantization (GPTQ, AWQ, FP8), tensor parallelism, and pipeline parallelism - Hands-on experience with multiple accelerator families (NVIDIA A100/H100, AWS Inferentia2, Trainium, AMD MI300X) and maintaining hardware-agnostic serving infrastructure - Multi-region deployment experience with geographic routing and failover for latency-sensitive inference endpoints - Proficiency in Rust or Go for performance-critical infrastructure components - SRE practices for ML systems: chaos engineering on GPU workloads, incident management, capacity modeling for bursty inference traffic - Experience with model registries, artifact versioning, and ML supply chain security - Observability platform expertise: building custom metrics for token-level throughput, time-to-first-token, and per-request GPU memory profiling - Prior startup/high-growth experience balancing velocity with reliability in rapidly scaling AI systems 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 $192,000 - $272,000 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 Director, Data Platform Engineering
Your Impact at LILA Lila is seeking a highly motivated and experienced engineering leader to lead a team responsible for our Lila's product data platform. You will own the data platform and infrastructure end-to-end — architecture, delivery, reliability, and developer/data scientist experience. Our mission is to deliver Scientific Super Intelligence through a reliable, scalable, and self-service infrastructure for data ingestion, storage, processing, and interaction — enabling AI/ML, product teams, and scientists to build data-intensive applications with confidence and speed. Our platform supports analytical and machine learning workloads across Lila, serving autonomous DBTL cycles, instrument data pipelines, and AI inference workflows. You will be responsible for building and leading a team of talented engineers, driving technical strategy, and ensuring the scalability and performance of our data management and data serving capabilities of our Data Platform. You will work closely with data scientists, data engineers, lab scientists, and product teams to understand their needs and deliver innovative solutions that leverage the power of cutting edge data processing technologies. What You'll Be Building - Team Leadership: Build, mentor, and manage a high-performing team of 30-40 data engineering experts. Evaluate and adopt modern data infrastructure - including real-time streaming (Kafka, Flink), columnar engines (DuckDB, ClickHouse), lake house, and cloud-native object storage architectures; Foster a culture of collaboration, innovation, and continuous improvement; Provide technical guidance and mentorship to team members, promoting their professional growth; Conduct performance reviews, provide feedback, and identify opportunities for training and development; Manage team workload, prioritize projects, and ensure timely delivery of high-quality solutions. - Technical Strategy and Execution: Define and execute the technical roadmap for our data platform, aligning with Lila’s overall data strategy; Drive innovation in data Lakehouse and data serving ecosystem exploring new technologies and approaches to improve usability, performance, scalability, and efficiency; Ensure the reliability, availability, and security of our data processing infrastructure.; Collaborate with other engineering teams to integrate our data processing technologies with other Lila systems and services. - Stakeholder Management: Partner with data scientists, data engineers, lab scientists, product managers, and other stakeholders to understand their data processing needs and requirements; Communicate technical concepts and solutions effectively to both technical and non-technical audiences; Advocate for best practices in data processing and engineering; Manage expectations and ensure alignment across different teams. - Engineering Thought Leadership: Represent Lila’s data platform work at external conferences; Deliver presentations, and write blog posts highlighting Lila’s leadership in big data processing. - Scientist and Engineering Productivity: Drive innovative, agentic, and low-code solutions to deliver data interfaces - exploration, query, analytics, and ML/inference solutions at scale. What You’ll Need to Succeed - 12+ years of software development experience, with a focus on data processing at scale. 5+ years of experience leading senior engineers. - Experience with building on AWS/GCP primitives like S3 + Athena/BigQuery, and query engines. - Operated data platforms at petabyte scale with sub-second query latency requirements. - Experience managing data infrastructure supporting 100+ concurrent ML training and inference workloads. - Familiarity with LLM/AI-native data patterns — vector stores, embedding pipelines, pre/mid/post training. - Track record of building data platforms in high-growth or early-stage environments where speed-to-value mattered as much as long-term architecture. - Hands-on coding in Python and modern backend frameworks. Experience with infrastructure-as-code and containerized deployments (Kubernetes). - BS, MS, or Ph.D. in Computer Science or a related field of study. Bonus Points For - Thought leadership in the community via presentations in conferences or blog posts. - Experience building and growing teams focusing on open source technologies. - Scientific data management and quality experience - Built self-service data products/platforms where developer experience was a first-class product concern. 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 $300,000 - $390,000 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 Product Manager, Fine-tuning
Your Impact at LILA You'll own two connected but distinct areas. First, you'll decide which new scientific domain capabilities the Lila model should gain and how to build them — the right mix of reinforcement-learning environments and supervised fine-tuning (SFT) data — prioritized against our Target Product Profiles (TPPs) and customer needs. Second, you'll own the roadmap and end-to-end delivery of fine-tuned Lila model variants for priority platform and commercial use cases, including the training data, evals, and release criteria needed to ship them. These capability priorities also feed the AI Research team's core-model training cycle as a structured input, though that cycle itself sits outside this role's direct ownership. You'll sit within the Research Product Manager function and work side-by-side with AI Research, joining their sprint planning and standups, to ensure the right capabilities are built and upstream into the Lila foundation model. What You'll Be Building - Own and maintain the prioritized backlog of training-capability packages (reinforcement-learning environments + SFT data), weighing Lila's TPPs, customer/commercial needs, and quarterly Lila model training needs - Own the roadmap and delivery of fine-tuned Lila model variants for priority platform and commercial use cases: define the required training data, strategic data gaps, evals, and release criteria - Coordinate the handoff of validated fine-tunes to the app and platform teams - Serve as a structured input into the AI Research team's core-model training cadence, which sits outside this role's direct ownership - Partner with AI Research (training-pipeline owners, evals, and data-mix leads) to translate prioritized capabilities into clear, well-specified requirements - Represent customer- and commercial-driven capability requests, and translate them into concrete capability asks the research team can execute against - Integrate into AI Research sprint planning and standups to track delivery and unblock cross-team dependencies - Define and track release/acceptance criteria for new capabilities in partnership with the evals workstream, so shipped work can be validated before it informs fine-tuning or Lila model training What You'll Need to Succeed - Experience as a Product Manager on ML/AI products, ideally with exposure to LLM training, fine-tuning, or RL-based capability development - Experience owning delivery of a model or product variant for external or commercial customers, including defining data, evals, and release criteria - Demonstrated ability to build and defend a prioritized backlog against competing scientific, research, and commercial demands - Strong cross-functional operator, comfortable partnering closely with a research organization (joining sprint planning/standups, not just handing off requirements) - Track record translating ambiguous or evolving priorities into a clear, sequenced set of deliverables Bonus Points For - Familiarity with capability-based training approaches for LLMs (RL environments, SFT data generation, evals) - Experience partnering with ML/AI research teams on eval design and release criteria - Background in a scientific or technical domain relevant to Lila's mission 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 $204,000 - $310,000 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.
Principal Scientist / Associate Director, Agentic...
Your Impact at LILA Own the technical direction for agentic AI systems applied to materials science at Lila. You will set and execute the roadmap for autonomous agents that plan, run, and interpret materials experiments, based on understanding of internal knowledge and state-of-the-art research work in public literature. Your work shifts materials research from human-paced iteration to machine-paced experimentation through scientific reasoning and understanding. This is a player-coach role on the PS AI team. You will lead a small group of scientists and engineers, set the bar for scientific rigor and engineering quality, and partner with diverse teams so that agentic systems land on real programs. You will own the trade-offs between research ambition and production reliability, and represent the agentic-AI direction to technical leadership. The work spans foundational research and applied delivery. You will publish where the science merits it, ship systems that materials teams depend on, and shape how Lila scales agentic capabilities across its materials portfolio. What You'll Be Building - Roadmap and direction. Define and execute the agentic AI roadmap for materials science, including agentic frameworks and retrieval-augmented generation for understanding multi-modal research data from research literature and other data sources. - Agent system architecture. Lead the design of agentic frameworks grounded in fundamental scientific understanding and the state of the art, and deliver end-to-end systems on real-world projects. - Team leadership. Hire, mentor, and grow a small cross-functional team of scientists and engineers; set the bar for scientific rigor, code quality, and reproducibility. - Cross-team partnership. Partner with diverse teams at Lila to push the state of the art and deliver systems that integrate with experimental infrastructure and land on real programs. - Research currency and external voice. Track state-of-the-art in agentic AI, scientific ML, data extraction, and reasoning models; translate external advances into internal direction, and publish or present where the science merits it. What You'll Need to Succeed - PhD in Computer Science, Machine Learning, Materials Science, Chemistry, Physics, or a related field, with 5+ years of post-PhD research and applied ML experience. - Track record of building and shipping agentic systems, ML pipelines, or autonomous research workflows that delivered measurable scientific or product impact. - Deep expertise across modern ML, NLP, and reasoning: LLMs, agentic frameworks, tool use, planning, data extraction, and multi-modal data. - Working knowledge of materials science, computational chemistry, or condensed-matter physics sufficient to ground agent behavior in real scientific constraints. - Proficiency in Python and the ML software stack, with strong engineering habits around reproducibility, testing, and production deployment. - Experience leading scientists and engineers: setting technical direction, hiring, mentoring, and developing team members. - Clear written and verbal communication; able to translate between research, engineering, and program stakeholders. Bonus Points For - Publications, patents, or open-source contributions in agentic AI, scientific ML, or autonomous research systems. - Experience integrating agents with real-world materials science tasks and familiarity with materials data representations and ontologies. - Production experience with workflow orchestration and distributed compute on cloud or HPC. - Community recognition: invited talks, conference organizing, or community leadership in agentic AI or scientific AI. 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 $288,000 - $420,000 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.
ML Scientist, Nucleic Acid Design
Your Impact at LILA Lila Sciences is seeking an ML Scientist I/II, Nucleic Acid Design to advance RNA and DNA sequence design. This scientist will develop models and design strategies for understanding and engineering nucleic acid sequences, including problems such as 3’ UTR optimization, 5’ UTR optimization, CDS optimization, and promoter / enhancer design. You’ll work at the intersection of machine learning, sequence modeling, experimental design, and platform development. The work spans both applied design campaigns and building next-generation models that improve how Lila generates, evaluates, and learns from nucleic acid sequence-function data. The ideal candidate brings strong ML judgment, curiosity about biological mechanisms, and enthusiasm for areas such as regulatory genomics, RNA biology, and sequence-to-function modeling. You’ll collaborate with experimental scientists, ML researchers, and platform teams to build models that connect nucleic acid sequence design to biological function and make these capabilities usable across Lila’s autonomous science platform. What You'll Be Building - Build ML models for RNA and DNA sequence design across regulatory and coding sequence contexts. - Develop methods spanning de novo generation, sequence property prediction, diverse set selection for experimental validation, and active learning strategies. - Deeply investigate the biological mechanisms of designed sequences and propose hypotheses about why they succeed or fail. Turn these insights into better models and future design principles. - Partner with experimental scientists to propose informative assays, validation strategies, and learning loops. - Collaborate with ML scientists and engineers across Lila to integrate nucleic acid design models into robust platforms and agent-driven frameworks. - Stay current with research in nucleic acid biology, sequence design, and scientific ML, and share research findings externally through papers or blog posts. What You'll Need to Succeed - PhD or equivalent experience in machine learning, computational biology, bioengineering, computer science, statistics, or a related quantitative field. - Hands-on experience building, training, and evaluating ML models for DNA or RNA. - Strong foundation in modern ML methods, with practical experience using frameworks such as PyTorch, JAX, or equivalent tools. - Experience developing models for sequence design, sequence-function prediction, generative modeling, or active learning. - Ability to reason about complex biological systems, scope ambiguous scientific problems, and formulate ML approaches that address difficult sequence-function challenges. - Curiosity about nucleic acid biology, including RNA biology, regulatory genomics, or related sequence-to-function problems. - Strong communication and collaboration skills, with a preference for team-based science and the ability to build shared technical direction across ML, engineering, platform, and experimental teams. Bonus Points For - Experience with regulatory element design, sequence-to-expression DNA models, or models trained on genomic, MPRA, STARR-seq, or related functional genomics data. - Experience with RNA sequence-function modeling, RNA secondary structure modeling, UTR design, or inverse design methods for RNA sequences. - Experience with sequence design in applied therapeutic contexts. - Experience collaborating with wet-lab teams to close the design-test-learn loop, including assay design, experimental prioritization, and interpretation of validation data. - Familiarity with high-throughput experimental datasets, pooled screens, reporter assays, or other sequence-function measurements. - Industry experience translating ML research into practical biological design workflows, experimental campaigns, or platform capabilities. 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.
Senior Product Manager, Experimentation Tooling
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 Senior Product Manager to own Lightning AI’s experimentation and post-training product end to end—from product strategy and roadmap through launch, adoption, pricing, and go-to-market. This is a role focused on how AI researchers and engineers turn an idea into a high-quality, validated model. You’ll define the workflow for running and comparing experiments, managing fine-tuning and reinforcement-learning workloads, evaluating model quality, understanding failures, and selecting the right model or checkpoint for production. You’ll work at the intersection of developer tooling, AI infrastructure, and model quality. The right candidate understands how modern AI teams work today: notebooks, training jobs, experiment trackers, checkpoints, evaluation suites, spreadsheets, and custom internal tooling. You can identify where those workflows break down and turn them into a cohesive, minimal product experience. You should be able to move fluidly between designing an intuitive developer workflow, discussing distributed training and artifact lineage with engineers, evaluating model outputs, and explaining the product’s value to customers and sales teams. This role requires unusually high ownership. You will work directly with engineering, customers, and the executive team; develop strong product opinions; create your own product artifacts; and drive work forward without waiting for another function to define the next step. You will join the Product Team, report to our VP of Product, and work directly with our executive team as we grow this business. This is a hybrid role based in our New York City or San Francisco office, with an in-office expectation of two days per week. What You’ll Do - Own the product vision and roadmap for post-training and experimentation — what we build, what we integrate with, what we don't build, and in what order - Understand how ML engineers and AI researchers actually work today: the jobs they run, the comparisons they make, the failures they debug, and the handoffs that break down between research and production — then build the product that makes that workflow coherent - Develop a strong point of view on where Lightning should build differentiated experiences versus integrate with the existing ecosystem of experiment trackers, evaluation frameworks, data tools, and model registries - Work directly with engineers from problem definition through architecture, implementation, and launch — understand the constraints, help shape the solutions, don't hand off requirements and wait - Use the product yourself; inspect failed workflows, read logs, identify friction, and remove it without waiting for someone to surface it - Own model evaluation as a product function — write evals, assess outputs, and let quality signals drive roadmap decisions - Design pricing and packaging in partnership with Growth and Finance — model unit economics, run experiments, and make calls that affect both adoption and margin - Build workflows that help teams collaborate: share results, compare models, move work from research into production, and maintain enough lineage that decisions can be explained and reproduced - Be the product voice in GTM — sales positioning, technical objection handling, and developer-facing content that builds credibility with ML engineers and platform teams - Define and instrument the metrics that matter across activation, iteration speed, compute consumption, retention, and expansion What You’ll Need - 7+ years of product management experience, including at least 3 years building infrastructure, platform, developer-tooling, or machine-learning products. - Hands-on experience building products for ML engineers, AI researchers, or data scientists. - A detailed understanding of experimentation and post-training workflows, including training jobs, checkpoints, metrics, artifacts, experiment comparison, reproducibility, and model evaluation. - Experience with one or more modern post-training techniques, such as supervised fine-tuning, preference optimization, reinforcement learning, distributed training, or hyperparameter optimization. - Experience designing or working closely with model evaluations. You understand that model quality is multidimensional and know how qualitative and quantitative signals should inform product decisions. - Strong product and interaction judgment. You can simplify technically complex workflows, develop a clear information architecture, and create usable product experiences without depending heavily on a dedicated design function. - Enough technical depth to work directly with infrastructure and ML engineers on APIs, SDKs, execution systems, distributed workloads, observability, data and artifact management, and failure handling. - A record of end-to-end ownership. You do not stop at roadmap definition; you investigate problems personally, create the necessary artifacts, drive decisions, support implementation, validate the result, and help bring the product to market. - Strong prioritization and willingness to say no. You can identify the narrowest valuable product wedge and resist building a broad collection of loosely connected features. - Experience owning pricing, packaging, or consumption-based products, including working with unit economics and making decisions that affect adoption and margin. - Strong written and verbal communication. You are equally comfortable writing a technical product specification, reviewing a developer workflow, presenting to executives, and explaining the product to a customer. - High bias for action and comfort operating in ambiguous, fast-moving environments with limited process and support. - BS in Computer Science, Engineering, or equivalent practical experience. Bonus Points - Experience at an AI infrastructure company, neocloud, hyperscaler ML platform, experiment-tracking company, or developer-tooling startup. - Familiarity with PyTorch, PyTorch Lightning, distributed training, GPU infrastructure, or large-scale fine-tuning. - Experience with tools such as Weights & Biases, MLflow, Hugging Face, Ray, Slurm, Kubernetes, or comparable internal ML platforms. - Experience building collaborative workflows for teams moving models from research through evaluation and production. 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: $160,000 - $275,000 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.
AI Platform Support Engineer (US)
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 Lightning AI is looking to hire an AI Platform Support Engineer to join our US Customer Experience team, supporting ML engineers running large-scale training and inference workloads across cloud infrastructure, Kubernetes, and GPU platforms in production environments. This role sits at the intersection of ML systems, cloud infrastructure, Kubernetes, and customers. You’ll support engineers training models, deploying inference systems, and scaling GPU workloads in production.You are not a ticket router or traditional support engineer. You are a technical partner to ML teams - helping diagnose failures, improve reliability, and guide customers through complex distributed systems problems. The problems range from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, networking bottlenecks, storage performance, and platform reliability. You’ll gain exposure to a wide variety of real world AI workloads across industries and help shape the infrastructure powering the next generation of ML applications. This role is hybrid out of our Seattle, San Francisco, or New York office hubs, with an in-office requirement of at least 2 days per week and occasional team and company offsites. The role follows a Monday–Friday schedule, with working hours from 8:00 AM to 5:00 PM PST. We are not able to provide visa sponsorship for this role at this time. What You'll Do Work Directly With ML Engineers - Partner directly with customer engineering teams running training and inference workloads in production - Help customers diagnose and resolve complex distributed systems and ML infrastructure issues - Act as a technical advisor during high impact incidents and platform degradation events - Translate infrastructure level issues into actionable guidance for ML engineers - Build credibility with customers through strong technical reasoning and clear communication Debug ML Infrastructure & Distributed Workloads - Investigate failures involving distributed training, Kubernetes orchestration, GPU allocation, networking, and storage systems - Troubleshoot PyTorch, CUDA, NCCL, and inference serving related issues - Analyze logs, metrics, traces, and system behavior to isolate root causes - Debug containerized workloads running across Kubernetes and bare metal GPU environments - Support customers scaling workloads across multi node GPU systems - Diagnose performance bottlenecks involving compute, memory, networking, or storage Improve Reliability & Platform Operations - Identify recurring patterns across customer issues and drive long term reliability improvements - Contribute to post incident reviews and operational improvements - Build internal tooling, automation, documentation, and runbooks - Partner closely with infrastructure, networking, and platform engineering teams - Help improve observability, operational visibility, and troubleshooting workflows - Improve the customer experience through better processes and technical guidance What This Role Is Not To set clear expectations: - This is not a traditional help desk or ticket routing support role - This is not purely customer success or account management - This is not a backend engineering role - This is not a passive escalation position This role is for engineers who enjoy solving difficult technical problems while working closely with other engineers. What You’ll Need Required Qualifications Infrastructure & Systems - Strong software engineering and systems troubleshooting background - Experience with Kubernetes and containerized environments - Linux systems knowledge, including networking, storage, process management, and performance tuning - Experience with cloud infrastructure and distributed systems - Experience with observability and debugging tools such as Prometheus, Grafana, or OpenTelemetry ML Infrastructure Experience - Hands on experience operating machine learning workloads in production or research environments - Experience with distributed ML systems and tooling such as PyTorch, CUDA, or NCCL - Familiarity with GPU infrastructure and orchestration - Experience troubleshooting performance, reliability, or scaling issues in ML infrastructure - Understanding of the operational challenges involved in running ML systems at scale Collaboration - Strong communication skills and ability to work directly with highly technical customers and engineering teams - Comfortable operating in fast moving, highly ambiguous environments - Enjoys solving complex technical problems collaboratively Ideal Experience - Experience with large scale model training or distributed inference systems - Familiarity with Ray, Kubeflow, Slurm, or similar distributed scheduling platforms - Experience with InfiniBand, RDMA, or high-performance networking - Experience operating bare metal infrastructure - Familiarity with storage systems commonly used in ML environments - Experience working at an AI infrastructure, cloud, MLOps, or developer tooling company - Contributions to platform engineering, developer infrastructure, or operational tooling projects - Experience writing automation, tooling, or scripts in Python or similar languages 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: $115,000 - $140,000 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.
AI Platform Support Engineer (APAC)
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 Lightning AI is looking to hire an AI Platform Support Engineer to join our APAC Customer Experience team, supporting ML engineers running large-scale training and inference workloads across cloud infrastructure, Kubernetes, and GPU platforms in production environments. This role is not a ticket router or traditional support engineer. You are a technical partner to ML teams - helping diagnose failures, improve reliability, and guide customers through complex distributed systems problems.The problems range from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, networking bottlenecks, storage performance, and platform reliability. You’ll gain exposure to a wide variety of real world AI workloads across industries and help shape the infrastructure powering the next generation of ML applications. This role is remote and open to candidates based in the Philippines or Singapore. We are hiring for a Sunday-Wednesday shift schedule, with working hours from 7:00 AM to 5:00 PM local time (UTC+8). What You'll Do Work Directly With ML Engineers - Partner directly with customer engineering teams running training and inference workloads in production - Help customers diagnose and resolve complex distributed systems and ML infrastructure issues - Act as a technical advisor during high impact incidents and platform degradation events - Translate infrastructure level issues into actionable guidance for ML engineers - Build credibility with customers through strong technical reasoning and clear communication Debug ML Infrastructure & Distributed Workloads - Investigate failures involving distributed training, Kubernetes orchestration, GPU allocation, networking, and storage systems - Troubleshoot PyTorch, CUDA, NCCL, and inference serving related issues - Analyze logs, metrics, traces, and system behavior to isolate root causes - Debug containerized workloads running across Kubernetes and bare metal GPU environments - Support customers scaling workloads across multi node GPU systems - Diagnose performance bottlenecks involving compute, memory, networking, or storage Improve Reliability & Platform Operations - Identify recurring patterns across customer issues and drive long term reliability improvements - Contribute to post incident reviews and operational improvements - Build internal tooling, automation, documentation, and runbooks - Partner closely with infrastructure, networking, and platform engineering teams - Help improve observability, operational visibility, and troubleshooting workflows - Improve the customer experience through better processes and technical guidance What This Role Is Not To set clear expectations: - This is not a traditional help desk or ticket routing support role - This is not purely customer success or account management - This is not a backend engineering role - This is not a passive escalation position This role is for engineers who enjoy solving difficult technical problems while working closely with other engineers. What You’ll Need Required Qualifications Infrastructure & Systems - Strong software engineering and systems troubleshooting background - Experience with Kubernetes and containerized environments - Linux systems knowledge, including networking, storage, process management, and performance tuning - Experience with cloud infrastructure and distributed systems - Experience with observability and debugging tools such as Prometheus, Grafana, or OpenTelemetry ML Infrastructure Experience - Hands on experience operating machine learning workloads in production or research environments - Experience with distributed ML systems and tooling such as PyTorch, CUDA, or NCCL - Familiarity with GPU infrastructure and orchestration - Experience troubleshooting performance, reliability, or scaling issues in ML infrastructure - Understanding of the operational challenges involved in running ML systems at scale Collaboration - Strong communication skills and ability to work directly with highly technical customers and engineering teams - Comfortable operating in fast moving, highly ambiguous environments - Enjoys solving complex technical problems collaboratively Nice-to-Haves - Experience with large scale model training or distributed inference systems - Familiarity with Ray, Kubeflow, Slurm, or similar distributed scheduling platforms - Experience with InfiniBand, RDMA, or high-performance networking - Experience operating bare metal infrastructure - Familiarity with storage systems commonly used in ML environments - Experience working at an AI infrastructure, cloud, MLOps, or developer tooling company - Contributions to platform engineering, developer infrastructure, or operational tooling projects - Experience writing automation, tooling, or scripts in Python or similar languages 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.
Technical Product Marketing Manager - Public Cloud
Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU. If you'd like to build the world's best AI cloud, join us. *Note: This position requires presence in our San Francisco or San Jose office location 4 days per week; Lambda’s designated work from home day is currently Tuesday. An ML team spins up an on-demand instance to prototype, outgrows it within a month, and needs a 512-GPU cluster with managed Slurm before their next training run. This role owns how Lambda meets them at every step: how the Public Cloud portfolio is positioned, packaged, launched, and enabled across self-service and sales-assisted motions. Lambda Cloud is a unified public cloud purpose-built and performance-optimized for AI workloads. The portfolio spans on-demand and reserved GPU instances, 1-Click Clusters, managed Kubernetes and Slurm, and supporting storage, networking, and platform capabilities. It serves customers ranging from AI-native startups and ML teams to enterprises and frontier labs. We're hiring a technical product marketing manager to own product marketing for the Public Cloud portfolio. You'll define how the portfolio is positioned, packaged, launched, and enabled across self-service and sales-assisted motions, working daily with product management, engineering, sales, solutions engineering, enablement, finance, and marketing. Product management owns product truth. You translate that truth into positioning, use cases, launches, and content that drive adoption and revenue. This role requires infrastructure depth. You can explain how GPU architecture, interconnect, storage throughput, data movement, orchestration, reliability, and cloud consumption models affect distributed training and inference. You can also communicate those tradeoffs to ML engineers, Enterprise architects, sales teams, and executive buyers. What You’ll Do - Own Public Cloud product marketing. Define and maintain positioning, messaging, audience segmentation, and the use-case library across the portfolio. Serve as the primary product marketing partner to the Public Cloud business unit, working with product management on roadmap context, packaging, and launch planning. - Build use cases and the portfolio GTM architecture. Translate training, fine-tuning, inference, and proof-of-concept workloads into documented, sellable use cases mapped to segment and infrastructure requirements. Establish how customers progress from on-demand evaluation to 1-Click Clusters and managed services. Clarify which offering fits each workload, segment, operational model, cluster size, and commercial commitment. - Execute product launches. Own launch execution for new product features, instance types, APIs, and managed services. Partner with product management and engineering from roadmap planning through general availability, including launch, audience definition, positioning, technical validation, content, field readiness, and post-launch analysis. - Create product-focused technical content. Produce messaging and positioning, solution briefs, datasheets, benchmark narratives, technical blogs, reference architectures, API and CLI walkthroughs, and use cases. Validate every technical claim with Product Management and Engineering. - Support sales and solutions engineering. Build discovery frameworks, qualification guidance, pitch materials, proposal content, battlecards, technical training, and competitive displacement narratives. Support active opportunities with workload- and segment-specific positioning. - Enable the broader marketing organization. Equip demand generation, growth, content, events, web, communications, and customer marketing with approved messaging, use cases, audience guidance, technical proof points, and launch materials. Review downstream content for consistency and technical accuracy. You - 7+ years of product marketing, technical marketing, product management, or related experience in cloud infrastructure, IaaS, managed services, or adjacent developer platforms - Demonstrated ownership of a technical product portfolio, including positioning, use-case development, product launches, competitive strategy, field enablement, and adoption - Deep working knowledge of AI infrastructure, with the ability to independently reason about GPU architectures, bare metal and virtualized compute, multi-node cluster topology, InfiniBand and RDMA fabrics, Ethernet networking, storage and data movement, Kubernetes and Slurm, APIs, observability, reliability, and cloud security - Demonstrated ability to produce technical content (benchmarks, whitepapers, architecture guides, datasheets, solution briefs, and technical demos) that withstands review by engineering audiences - Strong written and verbal communication across technical and GTM audiences, from engineers and infrastructure architects to sales leadership and executive buyers Nice to Have - Hands-on experience deploying AI or HPC workloads using a cloud console, API, CLI, Kubernetes, or Slurm - Familiarity with CUDA, PyTorch, NCCL, distributed training frameworks, and benchmarking methodologies such as MLPerf - Experience marketing managed Kubernetes, managed Slurm, GPU cloud infrastructure, or MLOps offerings - Previous experience as a product manager, solutions architect, infrastructure engineer, developer advocate, or technical sales engineer Salary Range Information The annual salary range for this position has been set based on market data and other factors. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description. About Lambda - Founded in 2012, with 500+ employees, and growing fast - Our investors notably include TWG Global, US Innovative Technology Fund (USIT), Andra Capital, SGW, Andrej Karpathy, ARK Invest, Fincadia Advisors, G Squared, In-Q-Tel (IQT), KHK & Partners, NVIDIA, Pegatron, Supermicro, Wistron, Wiwynn, Gradient Ventures, Mercato Partners, SVB, 1517, and Crescent Cove - We have research papers accepted at top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG - Our values are publicly available: https://lambda.ai/careers - We offer generous cash & equity compensation - Health, dental, and vision coverage for you and your dependents - Wellness and commuter stipends for select roles - 401k Plan with 2% company match (USA employees) - Flexible paid time off plan that we all actually use Equal Opportunity Employer Lambda is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.
Software Engineer, Agentic AI
About Invisible Invisible Technologies makes AI work. Our end-to-end AI platform structures messy data, automates digital workflows, deploys agentic solutions, measures outcomes, and integrates human expertise where it matters most. Our platform cleans, labels, and structures company data so it is ready for AI. It adapts models to each business and adds human expertise when needed, the same approach we have used to improve models for more than 80% of the world’s top AI companies, including Microsoft, AWS, and Cohere. Our successes span industries, from supply chain automation for Swiss Gear to AI-enabled naval simulations with SAIC, and validating NBA draft picks for the Charlotte Hornets. Profitable for more than half a decade, Invisible reached $134M in revenue and ranked as the number two fastest growing AI company on the 2024 Inc. 5000. In September 2025, we raised $100M in growth capital to accelerate our mission of making AI actually work in the enterprise and to advance our platform technology. About The Role We are seeking a highly skilled and driven Software Engineer with a strong background in full-stack development, particularly in backend technologies and Agentic AI, to join our AI/ML team. In this role, you’ll work at the intersection of engineering, data science, and real-world impact, partnering directly with clients and internal stakeholders to design and deploy ML-powered tools that solve meaningful problems. This role combines hands-on model development with robust backend engineering and infrastructure work. You’ll help build scalable systems, support R&D initiatives, and ensure rapid iteration and deployment of machine learning solutions in dynamic, production-ready environments. You will work embedded with our top clients and the client teams to conduct use case discovery with senior stakeholders directly, developing solutions that address complex client needs. What You’ll Do - As part of the Forward Deployed Engineering team, you’ll contribute during a phase of rapid growth as we focus on scaling models and improving platform performance. You’ll help build backend systems, support client-facing deployments, and enable smoother workflows for machine learning solutions. - Develop and Maintain AI/ML Systems: Build robust, scalable backend systems that support machine learning operations and data processing pipelines. - Cloud Operations and Management: Oversee and optimize cloud infrastructure to ensure efficient deployment and operation of ML models. - Problem Solving: Independently explore and address complex problem spaces to improve system capabilities and performance without extensive guidance. - Cross-Functional Collaboration: Work closely with ML engineers and data scientists to integrate advanced ML technologies, ensuring seamless operations across various platforms. - Client Engagement: Collaborate directly with Invisible’s clients, working embedded with client teams to support use case discovery, product development, and AI deployment. - Innovation and R&D: Actively participate in research and development of new tools that can enhance our AI capabilities and workflows. What We Need - 2+ years of software engineering experience, with a strong focus on ML engineering and deploying machine learning models in production. - Extensive experience in full-stack development, particularly in backend environments that support AI/ML workloads. - Prior experience working directly with clients in use case discovery, product development, and leading client engagements. - Technical Expertise: Strong proficiency in Python, with deep expertise in LLMs, AI Agents, and ML model development. - Experience designing and deploying scalable ML systems, such as retrieval-augmented generation (RAG) pipelines and production-grade AI applications. - Extensive experience with cloud platforms (AWS, GCP, Azure) and operational best practices for ML workloads. - Familiarity with Kubernetes and other container management tools. - Ability to write well-structured, organized code and automated unit/E2E tests. - Comfortable with polyglot persistence models (SQL vs. NoSQL). - ML Operations: Experience with MLOps frameworks and best practices; familiarity with DevOps principles as applied to machine learning models, including model versioning, monitoring, and lifecycle management. - Problem Solving: Ability to operate independently in unstructured environments, demonstrating a proactive and investigative approach to tackling challenges. - Communication: Excellent communication skills, with the ability to collaborate effectively in dynamic, cross-functional teams, including data scientists, researchers, and software engineers. What’s In It For You Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity. The annual salary range for this position, depending on level, is: R$ 249.000 - R$ 409.000 BRL You can find more information about our geographic pay tiers here . During the interview process, your Invisible Talent Acquisition Partner will confirm which tier applies to your location. For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living. *Bonuses and equity are included in all full-time offers. Final compensation is determined by a combination of factors, including location, job-related experience, skills, knowledge, internal pay equity, and overall market conditions. Because of this, every offer is unique. Additional details on total compensation and benefits will be discussed during the hiring process. What It's Like to Work at Invisible: At Invisible, we’re not just redefining work—we’re reinventing it. We operate at the intersection of advanced AI and human ingenuity, pushing the boundaries of what’s possible to unlock productivity and scale. Ownership is at the core of everything we do. Here, you won’t just execute tasks—you’ll build, innovate, and shape the future alongside world-class clients pushing the boundaries of AI. We expect bold ideas, relentless drive, and the ability to turn ambiguity into opportunity. The pace is fast, the challenges are big, and the growth is unmatched. We’re not for everyone, and we’re okay with that. If you’re looking for predictable routines, this isn’t the place for you. But if you’re driven to create, thrive in dynamic environments, and want a front-row seat to the AI revolution, you’ll fit right in. Country Hiring Guidelines: Invisible is a hybrid organization with offices and team members located around the world. While some roles may offer remote flexibility, most positions involve in-office collaboration and are tied to specific locations. Any location-based requirements or hybrid expectations will be communicated by our Talent Acquisition team during the recruiting process. AI Interviewing Guidelines: Our hiring team thoughtfully uses AI to support an efficient, engaging, and inclusive interview process. Since AI can also be a helpful tool for candidates, we've outlined expectations for using it ethically throughout your interview journey. Click here to learn more about how we use AI and our guidelines for candidates. Accessibility Statement: We are committed to providing reasonable accommodations for individuals with disabilities. If you require an accommodation to participate in the application or interview process, please submit your request using our accommodation request form. A member of our team will follow up to support your request. Here is the link the google form: https://docs.google.com/forms/d/e/1FAIpQLScqsu0PCQ0m0lwt-VFeXovNbZxWk0jogv8QI_ciGqZsIbSBjQ/viewform?usp=sharing&ouid=102356854798492362557 Equal Opportunity Statement: We’re an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or veteran status, or any other basis protected by law. Due to a high volume of candidates, Invisible may use automated decision-maker technologies to filter candidates based on response to our application questions and other provided information. Our use of automated decision-making enables us to be efficient by providing a manageable list of possible candidates that meet our mandatory hiring criteria. If you object to our use of automated decision-making please contact us.
Research Engineer
About Invisible Invisible Technologies makes AI work. Our end-to-end AI platform structures messy data, automates digital workflows, deploys agentic solutions, measures outcomes, and integrates human expertise where it matters most. Our platform cleans, labels, and structures company data so it is ready for AI. It adapts models to each business and adds human expertise when needed, the same approach we have used to improve models for more than 80% of the world’s top AI companies, including Microsoft, AWS, and Cohere. Our successes span industries, from supply chain automation for Swiss Gear to AI-enabled naval simulations with SAIC, and validating NBA draft picks for the Charlotte Hornets. Profitable for more than half a decade, Invisible reached $134M in revenue and ranked as the number two fastest growing AI company on the 2024 Inc. 5000. In September 2025, we raised $100M in growth capital to accelerate our mission of making AI actually work in the enterprise and to advance our platform technology. About The Role Invisible is building a reinforcement learning capability inside its research organization, focused on evaluation methodology, benchmarks, and RL environments for frontier labs and enterprise clients. As a Research Engineer, you'll work on how frontier models are measured, meaning the evaluations that labs and enterprises actually make decisions on, with direct influence over the methodology and not just its implementation. You'll take a research question (how do we measure whether a model can do this work, and how do we make that measurement reproducible?) and turn it into a scoring framework, an evaluation architecture, or an environment design, then ship the production system that runs it. This role doesn't hand specifications to someone else to build, and it doesn't only build what others have specified. It's a fit for someone who wants to design the approach and write the code that proves it out. Depending on level, the role reports to the Lead Research Engineer or, at Principal, directly to the VP of Research. What You'll Do - Design benchmarks and RL environments that measure real model capability for frontier labs and enterprise clients - Originate evaluation methodology and translate it into scoring frameworks, rubrics, and evaluation architectures - Write and ship the production code that implements your designs - Build and maintain the data pipelines that feed evaluation runs - Run the analysis that establishes whether a result holds, and make evaluations reproducible - Partner with Research Scientists on methodology review, with Solutions Architects on client requirements, and with ML software engineers who build and maintain the underlying platform What We Need - Production-quality code written daily; this is a hard requirement - Python & ML Stack: Fluency in Python and comfort across the modern ML stack - Evaluation & Infrastructure: Real experience building evaluation systems, RL environments, or training and inference infrastructure - Agentic Systems: Hands-on experience with modern agentic flows - RL & Frontier Evaluation: Familiarity with reinforcement learning methods and how frontier models are evaluated; we weigh this more heavily than years of experience - A track record of turning ambiguous research questions into working systems, and publishing or shipping the result You can find more information about our geographic pay tiers here . During the interview process, your Invisible Talent Acquisition Partner will confirm which tier applies to your location. For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living. *Bonuses and equity are included in all full-time offers. Final compensation is determined by a combination of factors, including location, job-related experience, skills, knowledge, internal pay equity, and overall market conditions. Because of this, every offer is unique. Additional details on total compensation and benefits will be discussed during the hiring process. What It's Like to Work at Invisible: At Invisible, we’re not just redefining work—we’re reinventing it. We operate at the intersection of advanced AI and human ingenuity, pushing the boundaries of what’s possible to unlock productivity and scale. Ownership is at the core of everything we do. Here, you won’t just execute tasks—you’ll build, innovate, and shape the future alongside world-class clients pushing the boundaries of AI. We expect bold ideas, relentless drive, and the ability to turn ambiguity into opportunity. The pace is fast, the challenges are big, and the growth is unmatched. We’re not for everyone, and we’re okay with that. If you’re looking for predictable routines, this isn’t the place for you. But if you’re driven to create, thrive in dynamic environments, and want a front-row seat to the AI revolution, you’ll fit right in. Country Hiring Guidelines: Invisible is a hybrid organization with offices and team members located around the world. While some roles may offer remote flexibility, most positions involve in-office collaboration and are tied to specific locations. Any location-based requirements or hybrid expectations will be communicated by our Talent Acquisition team during the recruiting process. AI Interviewing Guidelines: Our hiring team thoughtfully uses AI to support an efficient, engaging, and inclusive interview process. Since AI can also be a helpful tool for candidates, we've outlined expectations for using it ethically throughout your interview journey. Click here to learn more about how we use AI and our guidelines for candidates. Accessibility Statement: We are committed to providing reasonable accommodations for individuals with disabilities. If you require an accommodation to participate in the application or interview process, please submit your request using our accommodation request form. A member of our team will follow up to support your request. Here is the link the google form: https://docs.google.com/forms/d/e/1FAIpQLScqsu0PCQ0m0lwt-VFeXovNbZxWk0jogv8QI_ciGqZsIbSBjQ/viewform?usp=sharing&ouid=102356854798492362557 Equal Opportunity Statement: We’re an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or veteran status, or any other basis protected by law. Due to a high volume of candidates, Invisible may use automated decision-maker technologies to filter candidates based on response to our application questions and other provided information. Our use of automated decision-making enables us to be efficient by providing a manageable list of possible candidates that meet our mandatory hiring criteria. If you object to our use of automated decision-making please contact us.
Senior Manager, Imaging Machine Learning
THE OPPORTUNITY insitro's mission is to bring better drugs faster to patients who can benefit most, through machine learning and data at scale. Our discovery strategy integrates insights from multiple phenotypic readouts across diverse high-content data modalities, including data from public and proprietary human cohorts and in vitro cellular systems generated by our proprietary, automated wet-lab platforms. In this role, you will lead and grow a team of exceptional machine learning researchers responsible for co-developing methods for extracting information from in-vitro biology using computer vision and machine learning. You will lead an established team of 5 machine learning scientists, several at the Staff level, and be expected to grow it over time. Your team will partner closely with laboratory scientists to develop biological assays in a tight loop, iterating sample preparation protocols and feature extraction methods in tandem. As the manager of this team, you will be responsible for structuring these collaborations in ways that ensure the success of all teams, and your individual team members, as measured by our ability to drive insitro's therapeutic programs forward through understanding of causal human biology. In addition to working closely with the lab, you and your team will partner closely with our imaging software team, responsible for co-developing and scaling methods your team develops, and computational biologists who will subsequently leverage these tools to extract program-level insights in conjunction with omics (perturb-seq) and clinical insights. As the leader of a team of researchers within the AI/ML organization, you will be expected to remain abreast of the evolving field of in-vitro screening. You will report to the Director of Imaging, Cellular Machine Learning. This is a hybrid position that requires you to be in our South San Francisco headquarters at least three days per week. Join us, and help make a difference to patients! RESPONSIBILITIES - Lead, mentor, and grow a team of outstanding machine learning scientists to co-develop methods of extracting insights from in-vitro microscopy datasets - Work closely with laboratory leadership to structure assay development efforts, and empower members of your team to co-develop in-vitro microscopy assays suitable for genetic perturbation screening, often at whole-genome scale - Ensure the successful translation of assays you develop to insitro’s computational biology team, who will leverage the assays your teams develop to investigate causal biology through the integration of diverse data modalities, including microscopy - Co-develop the product vision for insitro’s industry-leading imaging software platform, and continue to support its development through method de-risking and translational development through adherence to established processes for contribution - Develop methods of measuring and qualifying microscopy data prior to model training and inference, including those which leverage agentic frameworks, VLMs, and self-supervised classification - Stay abreast of industrial trends and scientific literature in order to ensure insitro’s methods remain industry-leading and correctly matched to our applications. - Set yearly and quarterly strategic roadmaps for platform, set tactical goals for your team, and align with cross-functional stakeholders on a regular basis - Work shoulder-to-shoulder with your team where appropriate, participating in method/code review and authorship to model effective execution and cover gaps ABOUT YOU - Strong grounding in computer vision and machine learning fundamentals, with practical judgment about where these methods work and where they fail - Demonstrated experience applying CV/ML to pixel data to extract quantitative features and downstream biological insight - Working understanding of imaging principles — image formation, microscopy fundamentals, and the artifacts that arise from instruments and sample preparation - Hands-on experience with cellular imaging datasets (e.g. fluorescence/confocal or label-free microscopy) - 3+ years of people management, including solid-line management of multiple ICs and at least one senior (Staff-level) scientist or engineer - Track record of mentoring and growing technical talent day-to-day - Technical leadership as a decision-maker on projects, platforms, or teams - Strong communication and the ability to collaborate across functions, including with experimental and clinical life scientists NICE TO HAVE - Experience across additional high-content modalities: temporal phenotyping, spatial proteomics, single-cell or bulk omics, or pooled optical (in-situ) screening - Familiarity with genetic perturbation screening, ideally at genome scale - Experience with data-quality methods for imaging — agentic frameworks, VLMs, or self-supervised approaches for QC and classification - Conversational understanding of a relevant disease area (neuroscience, metabolic disease, or cancer biology) - Experience contributing to or shaping an imaging/CV software platform COMPENSATION & BENEFITS AT INSITRO Our target starting salary for successful US-based applicants for this role is $247,000 - $262,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data. This role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) and our Equity Incentive Plan, subject to the terms of those plans and associated policies. In addition, insitro also provides our employees: - 401(k) plan with employer matching for contributions - Excellent medical, dental, and vision coverage as well as mental health and well-being support - Open, flexible vacation policy - Paid parental leave of at least 16 weeks to support parents who give birth, and 10 weeks for a new parent (inclusive of birth, adoption, fostering, etc) - Quarterly budget for books and online courses for self-development - Support to attend professional conferences that are meaningful to your career growth and role's responsibilities - New hire stipend for home office setup - Monthly cell phone & internet stipend - Access to free onsite baristas and daily lunch for employees who are either onsite or hybrid - Access to a free commuter bus network that provides transport to and from our South San Francisco HQ from locations all around the Bay Area insitro is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. We believe diversity, equity, and inclusion need to be at the foundation of our culture. We work hard to bring together diverse teams–grounded in a wide range of expertise and life experiences–and work even harder to ensure those teams thrive in inclusive, growth-oriented environments supported by equitable company and team practices. All candidates can expect equitable treatment, respect, and fairness throughout the interview process. Please be aware of recruitment scams: we never request payments, all recruitment communications are from @insitro.com http://insitro.com, and if in doubt, contact us at info@insitro.com. #LI-Hybrid About insitro insitro is a drug discovery and development company using machine learning (ML) and data at scale to decode biology for transformative medicines. At the core of insitro’s approach is the convergence of in-house generated multi-modal cellular data and high-content phenotypic human cohort data. We rely on these data to develop ML-driven, predictive disease models that uncover underlying biologic state and elucidate critical drivers of disease. These powerful models rely on extensive biological and computational infrastructure and allow insitro to advance novel targets and patient biomarkers, design therapeutics and inform clinical strategy. insitro is advancing a wholly owned and partnered pipeline of insights and therapeutics in neuroscience and metabolism. Since launching in 2018, insitro has raised over $700 million from top tech, biotech and crossover investors, and from collaborations with pharmaceutical partners. For more information on insitro, please visit www.insitro.com http://www.insitro.com.
Principal Research & Engineering, Realtime Voice A...
About Inflection AI Inflection AI is a Public Benefit Corporation empowering people with human-centered, emotionally intelligent AI. We’re shaping the future of AI by combining emotional intelligence (EQ) and raw intelligence (IQ) to elevate people’s potential. Inflection AI created Pi, the world’s first emotionally intelligent AI, to help people work through decisions, emotions, and challenges. Pi is a personal AI agent powered by Inflection AI’s foundation model, proving that AI can be personal, empathetic, and contextually aware. About the Role Voice is becoming the highest-stakes interface for AI, where quality depends on speed, naturalness, interruption handling, emotional nuance, and reliability in real-world conditions. We are looking for a hands-on technical leader to define and build Inflection’s realtime Voice AI stack across speech models, streaming systems, voice-agent runtime, and evaluation. This person will help shape how emotionally intelligent AI shows up in spoken interactions, partnering across research, engineering, product, and design to deliver voice agents that feel responsive, trustworthy, and useful in enterprise settings. What You’ll Do - Establish the technical roadmap for Inflection's realtime Voice AI stack, encompassing streaming ASR, TTS, speech-to-speech, speech LLMs, turn-taking, barge-in, latency, and reliability. - Utilize a 1,000 GPU cluster to support performance benchmarking and extensive experimentation. - Determine build-vs-buy-vs-train strategies for core audio, speech, and realtime interaction components. - Direct research and engineering efforts focused on speech quality, naturalness, expressiveness, emotional fit, controllability, and production readiness. - Collaborate with infrastructure, product, design, and agentic AI teams to deploy voice agents for enterprise workflows. - Develop evaluation systems measuring voice quality through metrics such as clarity, emotional appropriateness, interruption handling, task success, user preference, latency, and reliability, moving beyond standard WER. - Refine production voice behavior by debugging across runtime, model, evaluation, data, and product layers. - Mentor, and Coach a team specializing in speech research, audio infrastructure, realtime systems, and evaluation. What We’re Looking For - Experience leading or serving as a principal Research and Engineering contributor to realtime voice, speech, audio AI, or conversational AI systems in production. - Experience with one or more of: streaming ASR, TTS, speech-to-speech systems, speech LLMs, audio tokenization, multimodal models, barge-in, low-latency inference, or realtime agents. - Strong technical judgment across both speech modeling and production systems. - Ability to define voice quality in terms of user and customer outcomes, not only offline model metrics. - Experience designing or using evaluation systems that capture real user experience. - Strong product intuition for natural, trustworthy, emotionally appropriate voice interactions. - Ability to lead senior technical talent while staying close to the code, architecture, and debugging work. - Have a bachelor’s degree or equivalent in a related field to the offered position requirements Employee Pay Disclosures At Inflection AI, we aim to attract and retain the best employees and compensate them in a way that appropriately and fairly values their individual contributions to the company. For this role, Inflection AI estimates a starting annual base salary to fall within the range of $400,000 to $550,000 , depending on a candidate’s qualifications and level of experience. This role also includes a meaningful equity component, allowing employees to share in the long-term success of the company. Benefits Inflection AI values and supports our team’s mental, emotional, financial and physical health. We are focused on building a positive, safe, inclusive and inspiring place to work. Our benefits include: - Robust medical, dental and vision options with employer contributions for HSA, FSA and DFSA - 401k matching program - Flexible Time Off, 10 paid holidays, 5 days sick leave - Parental, Medical and Family care leave - Generous cell-phone, wellness and office set up stipends - Support of country-specific visa needs for international employees living in the Bay Area
Secure data infrastructure for AI
At Inceptive, you will drive forward development that could help billions of people. To accomplish this, you will be part of a collaborative, antedisciplinary team building our biological software. Our AI models depend on rich, high-quality biological datasets. The integrity, security, and reliability of those datasets and of the infrastructure that supports them are critical to everything we do. As we scale, we need someone who can architect and own the systems that keep our data and our customer’s data safe, well-governed, and optimally accessible to our machine learning pipelines. This is a senior, hands-on role: you will design and build, not just advise. You will work closely with our ML researchers, data engineers, and computational biologists to understand data flows end to end. This includes data ingestion, training, inference, analysis, logging, result output, and model serving. Your work will help secure our infrastructure at every stage. It will also protect our most sensitive assets, including model configurations and weights, training data, and experimental results, from external adversaries and insider threats. Your Mission, should you choose to accept it - Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise - Architect, implement, and own secure data infrastructure supporting our AI model training and deployment pipelines — from raw data ingestion to model weight storage and access - Build and operate foundational security services: authentication systems, access brokers, secrets management, key management platforms, and egress/ingress controls across our multi-cloud environment - Design and enforce data governance frameworks, such as RBAC/ABAC policies, audit logging, encryption at rest and in transit, workload identity, and data lifecycle management - Embed security directly into our MLOps pipeline: CI/CD security controls, container and Kubernetes security, namespace isolation, and pod security standards - Conduct threat modeling and secure design reviews for existing and new systems, proactively identifying attack surfaces across the full AI tech stack - Identify, prioritize, and drive remediation of vulnerabilities across our data systems, cloud environments, and ML tooling, including AI-specific risks like data poisoning, model extraction, and unauthorized access to model weights - Build detection and alerting pipelines for anomalous data access patterns and potential exfiltration events - Establish security best practices and educate team members on secure coding, infrastructure patterns, and secure data handling for AI systems - Partner with ML and biology teams to ensure data handling practices meet the highest standards for sensitive research data Qualifications and Requirements - 7+ years of hands-on experience in data engineering, infrastructure security, or software security, ideally spanning both disciplines - Strong System and Software Engineering skills with production-quality code in Python, Bash, and at least one systems programming language (Go, Rust, or C++) - Deep experience securing GCP cloud environments, including IAM, VPC design, secrets management, workload authentication, and cloud security posture management - Proven track record designing and implementing identity and access management systems, including credential issuance, rotation, and least-privilege enforcement at scale - Hands-on experience with Kubernetes security: RBAC policies, namespace isolation, workload identity, pod security - Certifications (Highly Desirable): CISSP, OSCP, or GWAPT for core security credentialing, plus AI-focused certifications such as GAISC, Offensive ML (OffSec), or cloud provider AI security tracks (AWS/GCP). - Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET - Readiness to travel several times a year for company retreats and business events - We value the benefits of in-person collaboration and expect candidates to primarily work from our Palo Alto or Berlin offices Preferred Technical Skills - PhD or advanced degree in Computer Science, Electrical Engineering, or a related field or equivalent practical experience - Familiarity with AI/ML-specific security risks: data poisoning, model extraction, prompt injection, unauthorized model weight access, and adversarial attacks on training pipelines, and practical mitigations for each - Experience securing ML infrastructure, including model registries, training cluster access, dataset versioning, experiment tracking systems, and GPU compute environments - Proficiency with Terraform infrastructure-as-code and GitOps security practices, including automated misconfiguration detection and remediation - Experience with compliance frameworks relevant to sensitive research data (SOC 2, HIPAA, GDPR) and translating them into concrete engineering controls - Background in offensive security techniques, including threat modeling, penetration testing, vulnerability research, or red team exercises. In a nutshell, the ability to think like an adversary - Experience building detection pipelines for insider threats, data exfiltration, and anomalous access patterns - Knowledge of cryptographic protocols and their practical application in distributed systems (key management, TLS, secure enclaves) - Prior experience in a fast-moving startup or research environment where security must scale alongside rapid growth Compensation $200K – $275K + Bonus + Equity What we offer - A competitive compensation package - 30 days paid vacation per year - Comprehensive health insurance for US based Beginners - 401K with company match for US based Beginners and Direktversicherung for German Beginners - Quarterly company-wide retreats - Monthly wellness benefit - Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland - Learning & Development budget to attend conferences, take courses, or otherwise invest in your professional growth - A buddy to help you get settled *Varies by country and does not apply to internships At Inceptive, we are creating tools to develop increasingly powerful biological software for the rational design of novel, broadly accessible medicines and biotechnologies previously out of reach. Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming antedisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields. We approach our goals with a Beginner's mind, humbly and with fresh eyes, and aim to become the pioneers of a new discipline rooted in biology as much as in deep learning, whose impact will be realized together with out-of-the-box thinkers in business and entrepreneurship, defying established categorizations. We are building a company culture centered around growth, learning, and discovery. We believe in humility and open-mindedness in how we approach each other, as well as problems we don't yet have solutions for. It is the policy of Inceptive to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Inceptive prohibits any such discrimination or harassment. Inceptive is also committed to welcoming and providing accommodations to people with disabilities. Please let us know if you need any accommodations throughout your application process.
Senior Open-Source Python Engineer, ML Developer T...
At Hugging Face, we're on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 11 million users who collectively shared over 4 million models, 1 million datasets & 1.5 million Gradio apps. Our open-source libraries have more than 700,000 stars on Github. About the Role Roles at Hugging Face are very fluid and dynamic — we are looking for someone who is comfortable taking on different challenges that will evolve over time. You'll join the Applications team, whose goal is to build the tooling that helps developers create machine learning applications on top of the primitives we offer on the Hub: Gradio , Spaces , Inference Providers, Datasets, Xet, and more. Concretely, that means maintaining and growing libraries like Trackio , a local-first, agent-friendly experiment tracking platform, FastRTC , our library for real-time audio and video streaming in Python, and Gradio itself, used by more than 1 million developers each month. These tools have made machine learning accessible to a wide audience — for the first time in history, anyone with an internet connection can use cutting-edge models right from their browser. Several of these libraries are still early in their life, and new ones will start from scratch. A large part of this role is taking a project from its first users to widespread adoption: shaping the API, shipping fast, and getting it into developers' hands. Key responsibilities include: Designing, developing, and maintaining core features across the team's libraries, ensuring scalability, reliability, and ease of use for ML developers. Architecting complex Python projects and frameworks that serve as foundational tools for the ML community. Growing early-stage libraries: defining the API surface, writing the demos and documentation that make them click, and driving adoption in the developer community. Working on real-time streaming and networking capabilities, a core priority for Gradio 7 and beyond. Collaborating with open-source contributors and engaging with the broader community through code reviews, discussions, and support. Integrating modern frontend technologies to create seamless and intuitive user experiences for Python-based web applications. Adapting to evolving engineering challenges and contributing to the direction of our developer tools as the landscape changes. Requirements If you love open-source, are passionate about making machine learning more accessible, have experience building Python frameworks, and want to contribute to some of the fastest-growing libraries in the ML ecosystem, then we can't wait to see your application! What you'll need Bachelor's degree or equivalent in Computer Science or a related field 5+ years of professional Python development experience Experience architecting complex Python projects or frameworks A track record of taking early-stage libraries or products from first users to broad adoption Professional experience with JavaScript or TypeScript Professional experience with a modern frontend framework such as React or Svelte Experience core-contributing to or maintaining open-source software projects Nice to have Experience with real-time audio/video streaming or networking (WebRTC, WebSockets) Contributions to libraries such as pipecat , LiveKit , FastAPI , or Starlette If you're interested in joining us, but don't tick every box above, we still encourage you to apply! We're building a diverse team whose skills, experiences, and backgrounds complement one another. We're happy to consider where you might be able to make the biggest impact. One more thing At Hugging Face we believe great AI shouldn't require a massive cluster, we build for everyone, especially the GPU-poor. And because we read every application, here's a small sign that you read this one too: start your answer to the first application question with the words “ GPU-poor and proud 🤗”. No trick, no catch, it just tells us a real person is on the other side. Benefits More about Hugging Face We are actively working to build a culture that values diversity, equity, and inclusivity . We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We value development. You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education. We care about your well-being. We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off. We support our employees wherever they are . While we have office spaces in NYC and Paris, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed. We want our teammates to be shareholders . All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside. We support the community . We believe major scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community.
Senior Open-Source Python Engineer, ML Developer T...
At Hugging Face, we're on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 11 million users who collectively shared over 4 million models, 1 million datasets & 1.5 million Gradio apps. Our open-source libraries have more than 700,000 stars on Github. About the Role Roles at Hugging Face are very fluid and dynamic — we are looking for someone who is comfortable taking on different challenges that will evolve over time. You'll join the Applications team, whose goal is to build the tooling that helps developers create machine learning applications on top of the primitives we offer on the Hub: Gradio , Spaces , Inference Providers, Datasets, Xet, and more. Concretely, that means maintaining and growing libraries like Trackio , a local-first, agent-friendly experiment tracking platform, FastRTC , our library for real-time audio and video streaming in Python, and Gradio itself, used by more than 1 million developers each month. These tools have made machine learning accessible to a wide audience — for the first time in history, anyone with an internet connection can use cutting-edge models right from their browser. Several of these libraries are still early in their life, and new ones will start from scratch. A large part of this role is taking a project from its first users to widespread adoption: shaping the API, shipping fast, and getting it into developers' hands. Key responsibilities include: Designing, developing, and maintaining core features across the team's libraries, ensuring scalability, reliability, and ease of use for ML developers. Architecting complex Python projects and frameworks that serve as foundational tools for the ML community. Growing early-stage libraries: defining the API surface, writing the demos and documentation that make them click, and driving adoption in the developer community. Working on real-time streaming and networking capabilities, a core priority for Gradio 7 and beyond. Collaborating with open-source contributors and engaging with the broader community through code reviews, discussions, and support. Integrating modern frontend technologies to create seamless and intuitive user experiences for Python-based web applications. Adapting to evolving engineering challenges and contributing to the direction of our developer tools as the landscape changes. Requirements If you love open-source, are passionate about making machine learning more accessible, have experience building Python frameworks, and want to contribute to some of the fastest-growing libraries in the ML ecosystem, then we can't wait to see your application! What you'll need Bachelor's degree or equivalent in Computer Science or a related field 5+ years of professional Python development experience Experience architecting complex Python projects or frameworks A track record of taking early-stage libraries or products from first users to broad adoption Professional experience with JavaScript or TypeScript Professional experience with a modern frontend framework such as React or Svelte Experience core-contributing to or maintaining open-source software projects Nice to have Experience with real-time audio/video streaming or networking (WebRTC, WebSockets) Contributions to libraries such as pipecat , LiveKit , FastAPI , or Starlette If you're interested in joining us, but don't tick every box above, we still encourage you to apply! We're building a diverse team whose skills, experiences, and backgrounds complement one another. We're happy to consider where you might be able to make the biggest impact. One more thing At Hugging Face we believe great AI shouldn't require a massive cluster, we build for everyone, especially the GPU-poor. And because we read every application, here's a small sign that you read this one too: start your answer to the first application question with the words “ GPU-poor and proud 🤗”. No trick, no catch, it just tells us a real person is on the other side. Benefits More about Hugging Face We are actively working to build a culture that values diversity, equity, and inclusivity . We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We value development. You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education. We care about your well-being. We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off. We support our employees wherever they are . While we have office spaces in NYC and Paris, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed. We want our teammates to be shareholders . All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside. We support the community . We believe major scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community.
LLM Inference Engineer (Mid, Sr, Staff)
ROLE MISSION As HAI's LLM Inference Engineer, you will own the serving infrastructure that determines whether our breakthrough healthcare AI reaches patients efficiently and reliably. You'll optimize the systems that translate raw model capability into sub-100ms responses—making the difference between conversational experiences that feel natural and those that feel broken. This role exists because inference optimization at scale is where research meets reality: your work directly determines latency, cost, and availability for millions of patient conversations across healthcare systems. WHAT YOU WILL ACCOMPLISH Own your first major outcome: By day 90, you will have shipped a measurable improvement to our inference serving stack (reduced latency, improved throughput, or optimized cost per inference), validated the gains across our production deployment scenarios, and established the performance optimization roadmap that will guide infrastructure investment. Drive lasting impact: At 12 months, you will have designed and deployed advanced serving architectures (disaggregated inference, optimized caching, speculative decoding) that meaningfully improve patient experience and operational efficiency, contributed novel optimization techniques that become part of our core infrastructure, and made our serving stack a durable competitive advantage in healthcare AI deployment. THE TEAM You'll work alongside systems engineers, ML researchers, and infrastructure experts who are obsessed with making AI systems fast, reliable, and cost-effective. This is a team that values deep technical rigor, continuous benchmarking, and solving hard systems problems that have real impact on patient experience and business unit economics. WHAT YOU'LL DO - Design and implement multi-node serving architectures for distributed LLM inference - Optimize multi-LoRA serving systems - Apply advanced quantization techniques (FP4/FP6) to reduce model footprint while preserving quality - Implement speculative decoding and other latency optimization strategies - Develop disaggregated serving solutions with optimized caching strategies for prefill and decoding phases - Continuously benchmark and improve system performance across various deployment scenarios and GPU types LOCATION REQUIREMENT We believe the best ideas happen together. This role is based in our Menlo Park, California office, expected to be in office five days a week. COMPENSATION Compensation is based on experience, expertise, and level of responsibility. We offer competitive packages that reflect the seniority and scope of the role, along with equity, health insurance, and other benefits. WHAT YOU BRING MUST-HAVE: - Experience optimizing LLM inference systems at scale - Proven expertise with distributed serving architectures for large language models - Hands-on experience implementing quantization techniques for transformer models - Strong understanding of modern inference optimization methods, including: - Speculative decoding techniques with draft models - Eagle speculative decoding approaches - Proficiency in Python and C++ - Experience with CUDA programming and GPU optimization NICE-TO-HAVE: - Contributions to open-source inference frameworks such as vLLM, SGLang, or TensorRT-LLM - Experience with custom CUDA kernels - Track record of deploying inference systems in production environments - Deep understanding of performance optimization systems Show us what you've built: Tell us about an LLM inference or training project that makes you proud! Whether you've optimized inference pipelines to achieve breakthrough performance, designed innovative training techniques, or built systems that scale to billions of parameters - we want to hear your story. Open source contributor? Even better! If you've contributed to projects like vllm, sglang, lmdeploy or similar LLM optimization frameworks, we'd love to see your PRs. Your contributions to these communities demonstrate exactly the kind of collaborative innovation we value. Join a team where your expertise won't just be appreciated—it will be celebrated and amplified. Help us shape the future of AI deployment at scale! References 1. Polaris: A Safety-focused LLM Constellation Architecture for Healthcare, https://arxiv.org/abs/2403.13313 2 https://arxiv.org/abs/2403.133132. Polaris 2: https://www.hippocraticai.com/polaris2 3 https://www.hippocraticai.com/polaris23. Personalized Interactions: https://www.hippocraticai.com/personalized-interactions 4 https://www.hippocraticai.com/personalized-interactions4. Human Touch in AI: https://www.hippocraticai.com/the-human-touch-in-ai 5 https://www.hippocraticai.com/the-human-touch-in-ai5. Empathetic Intelligence: https://www.hippocraticai.com/empathetic-intelligence https://www.hippocraticai.com/empathetic-intelligence6 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.
AI Engineer
WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. About the Role We’re looking for a new-generation AI builder — someone between a software engineer, prompt engineer, automation hacker, and product innovator. This is not a traditional SWE role. You’ll work on AI-native products, internal agents, automations, and experimental workflows powered by the latest frontier models, open-source tooling, MCP ecosystems, and multimodal systems. You should be the kind of person who constantly explores what’s happening in AI: - new agent frameworks - open-source tooling - AI video & creative workflows - MCP servers - coding agents - automation stacks - multimodal systems - new product patterns emerging every week You don’t just follow trends — you prototype them. WHO WE’RE LOOKING FOR You might be a fit if you: - Are a hustler who ships fast and experiments constantly - Have strong intuition for building new AI-native products and workflows - Stay deeply plugged into the AI ecosystem and emerging tooling - Understand how modern AI agents actually work under the hood - Can automate things quickly and creatively - Think in systems and workflows, not just prompts - Have strong product taste and curiosity - Thrive in fast-moving, ambiguous environments - Learn new tools obsessively fast MUST-HAVE SKILLS - Experience with JavaScript or Python (ideally both) - Comfortable building prototypes, automations, and internal tools - Understanding of: - LLMs - tokens/context windows - APIs & CLI tooling - MCP ecosystems - agent frameworks - tool use & orchestration - RAG / retrieval concepts - Experience using modern AI coding workflows and AI-native tooling - Ability to quickly turn ideas into working demos or systems BIG PLUS - Experience with ML or training/inference infrastructure - Experience with multimodal or video-generation tooling - Familiarity with open-source AI ecosystems - Experience building agentic systems or autonomous workflows - Strong vibe-coding energy WHAT YOU’LL WORK ON - Build AI-native internal and external products - Prototype new agentic workflows and automation systems - Explore frontier AI tooling and identify new product opportunities - Experiment with multimodal generation systems - Build automations across engineering, creative, and operational workflows - Work closely with product, infra, and research teams - Help the company stay ahead of where the AI market is going THE DEAL - Top-of-market compensation in USD. - Small team, high autonomy, and outsized impact. - On-site role in our Almaty office (we will relocate you from anywhere).
AI Research Engineer - GPU Simulation
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 Join our GPU simulation team and build the simulator that trains our reinforcement-learning agents for air autonomy. You will shape the simulator that turns complex air-combat scenarios into learning environments at scale, model the physics behind air combat, and support the transfer of those policies to the real aircraft. Your work will span flight model fidelity, scaling to thousands of parallel environments, and the training tooling that makes it all possible. This role sits at the meeting point of AI, GPU computing and physics-based simulation, with room to grow into a leadership position as the team matures. The day-to-day - Build, extend, and scale our GPU-based simulator to train reinforcement-learning agents across thousands of parallel environments - Develop the tooling that underpins RL training, including domain randomisation, to improve sim-to-real generalisation - Improve the fidelity and performance of simulation models, from flight dynamics to sensing - Enable sim-to-real transfer by supporting the export of trained policies and assets into downstream simulators and, ultimately, onto physical hardware - Collaborate with AI specialists and engineers to align the simulator with training needs and mission-relevant outcomes You should apply if you - Hold an MSc or PhD in Computer Science, Machine Learning, Robotics, Control Engineering, or a closely related field - Possess solid software engineering skills, writing clean and well-structured code in Python and/or languages like Rust or modern C++, and have experience deploying software to production including testing, and monitoring - Bring depth in at least one of the following: GPU programming, high-performance simulation, physics or flight-dynamics modelling, or navigation and control - Own high-quality, maintainable code and shape the shared library and ecosystem that the wider team builds on - Care about our mission and want your work to have real operational impact Note: We operate in an industry 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 - Have solid AI knowledge and can reason about how models are trained and evaluated - Have knowledge breadth across the specialist areas above, for example combining GPU programming with simulation environment design - Have deep GPU expertise, such as writing and optimising CUDA kernels, or expertise in libraries such as Taichi, NVIDIA Warp/CuteDSL, Triton or Helion 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 “Infraduction”), in which you will be building tooling and applications to be 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 engineering teams from day one (Specifically for engineering and AI) 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 . Helsing's Candidate Privacy and Confidentiality Regime can be found here .
Staff Software Engineer, Model Infrastructure
WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW As a Staff Software Engineer on the Model Infrastructure team, you'll lead the design and development of the systems that power every AI request at Harvey. You'll partner closely with AI Research, Product Engineering, Infrastructure, and external model providers to build a platform that is highly reliable, scalable, observable, and efficient. WHAT YOU’LL DO - Lead the design and implementation of Harvey's Model Infrastructure platform. - Build systems to ensure high availability, low latency, and operational excellence for AI inference. - Design and improve Harvey's Unified Model Controller (UMC) and Model Selector platform to automatically detect model degradations and intelligently route traffic based on reliability, latency, quality, compliance, and cost. - Develop systems for model provisioning, capacity management, failover, and traffic engineering across multiple AI providers. - Integrate new model providers and maintain provider APIs and SDKs, enabling Harvey to rapidly adopt emerging frontier models. - Improve observability through health dashboards, alerting, token usage analytics, cost reporting, and end-to-end telemetry. - Partner with Product Engineering to support model launches, experimentation, and proactive monitoring of production AI workloads. - Drive infrastructure efficiency through capacity planning, utilization optimization, and cost visibility. - Collaborate with AI Research to build the infrastructure foundation for future model evaluation, training, and deployment. - Lead cross-functional technical initiatives and mentor engineers across the organization. WHAT YOU'LL BUILD You'll help build the core platform behind Harvey's AI capabilities, including: MODEL RELIABILITY & OPERATIONS - Model health monitoring - Automated failover and recovery - Capacity provisioning - Operational tooling and incident automation UNIFIED MODEL CONTROLLER (UMC) - Policy-based model routing - Intelligent Model Selector - Model health monitoring - Traffic management - Reliability and latency optimization PROVIDER PLATFORM - Multi-provider architecture - API and SDK integrations - OpenAI, Anthropic, Azure OpenAI, Fireworks, Baseten, and future providers - Rapid adoption of new frontier models OBSERVABILITY & COST PLATFORM - Token usage analytics - Cost attribution - Latency and reliability dashboards - Capacity forecasting - Utilization optimization AI PLATFORM FOUNDATION - Infrastructure supporting model evaluation - Model deployment and operations - Future model training platform - Agent infrastructure and CcaaS WHAT YOU HAVE - 7+ years of software engineering experience building large-scale distributed systems. - Experience designing and operating highly available production services. - Strong programming skills in Go, Java, Python, Rust, or C++. - Deep understanding of distributed systems, cloud infrastructure, networking, and observability. - Experience leading technical projects across multiple engineering teams. - Ability to balance long-term architecture with pragmatic execution. - Strong communication and collaboration skills. - Passion for building foundational platforms that enable other engineering teams. NICE TO HAVE - Experience with AI infrastructure, LLM serving, or machine learning platforms. - Experience with model routing, inference gateways, or policy-based serving systems. - Experience working with OpenAI, Anthropic, Azure OpenAI, Fireworks, Baseten, or open-source LLMs. - Experience with Kubernetes, cloud infrastructure, and service mesh technologies. - Experience with large-scale observability and SRE best practices. - Experience with data infrastructure technologies such as Kafka, Spark, Flink, Airflow, or Iceberg. - Familiarity with GPU infrastructure or model training platforms. COMPENSATION RANGE $236,000 - $290,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-AN2 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai
Member of Technical Staff, AI Engineering
ABOUT HANDSHAKE Handshake's mission is to organize expert human knowledge to advance the AI economy. Handshake AI works directly with frontier labs on their most consequential data, evaluation, and post-training challenges, building the systems that turn expert human knowledge into the data and evaluations that make frontier models better. You will work alongside engineers, researchers, operators, and builders from organizations including Scale AI, Meta, Google, Amazon, xAI, Notion, and Palantir—and help build the systems that make expert human knowledge useful for advancing AI. ABOUT HANDSHAKE LABS Handshake Labs is building external AI products, research platforms, and customer-facing AI systems. We are evolving work that is often custom-built for an individual partner into reusable products and platforms that improve with every deployment. Our work spans the full post-training loop: designing evaluations and training environments, building high-quality data and feedback systems, running experiments, and turning what works into durable infrastructure. For example, we are developing agents that can analyze long, complex coding-agent sessions in days rather than weeks—with expert review and calibration built into the system. THE ROLE We are hiring a Member of Technical Staff to help build the data systems that make frontier model training possible. The data Handshake builds for and acquires on behalf of labs is getting more complex and more sensitive, and this role is responsible for improving how we generate, process, and prepare that data—whether that means building higher-quality synthetic and LLM-generated training data, or making acquired third-party data safe to use by removing personal information while preserving the structure that makes it valuable. You will partner with researchers, domain experts, legal/compliance stakeholders, and customers to turn ambiguous data questions—about generation, quality, evaluation, or privacy—into experiments, pipelines, and durable products. Early members of the team will have unusual influence over our technical direction, standards, and culture. Location: San Francisco & Mountain View preferred; open to exceptional candidates in other locations (London, Canada, Bangalore, etc.) WHAT YOU’LL DO - Design and build systems that improve the quality, scale, and safety of the data Handshake generates and acquires for frontier model training—spanning synthetic data generation and data anonymization/PII removal. - Translate ambiguous research, partner, or compliance needs into clear hypotheses, experiments, evaluation plans, and production-quality implementations. - Build and improve data-processing pipelines, evaluation frameworks, benchmarks, and quality-control systems, whether the goal is generating higher-signal synthetic data or verifying that sensitive data has been properly de-identified. - Run fast, rigorous iteration loops: prototype, evaluate, interpret results, and turn learnings into the next system or product. - Partner directly with researchers, domain experts, and—where relevant—legal and compliance teams to ensure data is both high-utility and responsibly handled. - Identify repeatable patterns across engagements and productize them into reusable software and platforms. - Raise the technical bar through strong design judgment, clear communication, code quality, and mentorship. WHAT WE’RE LOOKING FOR - 2–10 years of recent, demonstrated experience in one or more of: synthetic/LLM-generated data, post-training and model-evaluation work, privacy engineering, or data anonymization/de-identification at scale. - A hands-on individual contributor track record—this is not a team-lead or engineering-management role. - Strong Python skills and the ability to write clean, efficient, scalable software for large, messy, real-world datasets. - Sound judgment for reasoning about data quality, risk, and utility—forming hypotheses, choosing meaningful metrics, diagnosing failures, and distinguishing signal from noise. - Experience designing systems—not only implementing specifications—including tradeoffs around quality, scale, reliability, and reuse. - Comfort operating in an ambiguous, fast-moving environment with substantial ownership. - Collaborative, low-ego communication and the ability to work effectively with researchers, engineers, domain experts, and customers. ESPECIALLY COMPELLING EXPERIENCE - Building or operating large-scale synthetic or LLM-generated data pipelines for model training. - Building or operating large-scale data de-identification or anonymization systems, ideally involving relational or graph-structured data, with experience preserving referential/relationship integrity after anonymization. - Developing LLM/agent benchmarks, evaluation methodologies, annotation systems, or data-quality frameworks. - Research or applied work on reinforcement learning, alignment, model behavior, synthetic data, or human-in-the-loop systems. - Prior work in a regulated or high-sensitivity data environment (healthcare, finance, HR/people data, government), or experience with re-identification risk assessment and privacy auditing. - Published research, meaningful open-source contributions, or evidence of technical leadership in ML systems, data engineering, or AI research. - Experience productizing research or repeated customer work into robust, reusable platforms. WHY JOIN - Work on problems at the center of how frontier AI systems improve, alongside leading labs and domain experts. - Help build an early technical organization where your work shapes the roadmap, standards, and culture. - Move fluidly from research insight to real-world systems, with the resources and customer context to see those systems matter. - Join a company building durable infrastructure for careers in the AI economy. - Perks Handshake delivers benefits that help you feel supported—and thrive at work and in life. The below benefits are for full-time US employees. 🎯 Ownership: Equity in a fast-growing company 💰 Financial Wellness: 401(k) match, competitive compensation, financial coaching 🍼 Family Support: Paid parental leave, fertility benefits, parental coaching 💝 Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend 📚 Growth: $2,000 learning stipend, ongoing development 💻 Office: Commuting support, free lunch, and gym in our SF office 🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days 🤝 Connection: Team outings & referral bonuses
Principal Product Marketing Manager (Glean Intelli...
About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. About the Role: Glean is looking for a Principal Product Marketing Manager, Glean Intelligence to define and execute the go-to-market strategy for the technologies that power reliable, enterprise-grade AI. This role will translate advances in models, evaluations, agent harnesses, and Model Hub capabilities into clear product narratives, differentiated market positioning, and practical resources that help customers understand and adopt Glean Intelligence. You will work at the intersection of product, engineering, research, sales, and communications. You will stay close to the rapidly evolving AI landscape, develop a strong point of view on where the market is going, and bring Glean’s technical advantages to life through launches, customer communications, thought leadership, technical collateral, and field enablement. This is a highly visible, hands-on role for a product marketer who can engage deeply with technical teams, understand how modern AI systems work, and communicate complex ideas with precision and impact. You will help establish Glean as a technical leader in the next generation of enterprise AI. You will: - Own the product marketing strategy and go-to-market plans for Glean Intelligence, including model releases, Model Hub, evaluations, evaluation harnesses, and the systems that enable high-quality AI experiences. - Partner with Product and Engineering to translate architecture, technical tradeoffs, roadmap priorities, and customer value into differentiated positioning, messaging, launch plans, and customer-ready narratives. - Lead end-to-end launches for new models and platform capabilities—from audience and release strategy through communications, technical documentation, field enablement, customer education, and post-launch adoption—while establishing repeatable launch processes. - Define Glean’s point of view on model quality, evaluations, benchmarking, reliability, safety, observability, and enterprise AI deployment; create compelling technical content and customer stories that show how teams can measure, improve, and trust AI applications and agents. - Bring Model Hub to market by clarifying model choice, performance, cost, latency, and use-case fit; monitor industry and competitive developments; represent Glean Intelligence as a technical spokesperson; and equip Sales, Solutions Engineering, Customer Success, and Partners with the context and proof points they need to drive adoption. About you: - 8+ years of B2B SaaS product marketing experience, or related field - BA/BS in business, marketing, liberal arts, or related degree - Bring significant experience in AI infrastructure, enterprise software, data platforms, or a related technical category; experience with AI models, model platforms, evaluation systems, agent frameworks, or developer tools is strongly preferred. - Understand modern AI systems deeply, including LLMs, multimodal models, inference, context windows, RAG, embeddings, agents, model routing, evaluations, benchmarking, and enterprise data architectures. - Closely follow the AI industry and bring technical credibility with Product, Engineering, Research, developers, and technical buyers, translating complex technical decisions and market developments into clear customer and business implications. - Are an exceptional technical storyteller and writer who can turn architecture, evaluation results, and product capabilities into compelling narratives and collateral without oversimplifying the technology. - Have a track record of bringing complex technical products to market, communicating effectively with audiences ranging from developers to executives, and influencing cross-functional teams across Product, Engineering, Sales, Customer Success, Communications, and Brand. - Are rigorous about accuracy and evidence, energized by ambiguity, and able to balance strategic thinking with hands-on execution; a technical bachelor’s degree or equivalent practical experience is preferred, and an MBA is a plus. Location: - This role is hybrid (4 days a week in our Mountain View office or San Francisco office) Compensation & Benefits: The standard base salary range for this position is $224,000 - $258,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused. We’re committed to building and sustaining a diverse, inclusive workplace. We strive to attract and retain people with a wide range of backgrounds, experiences, and perspectives, and we do not discriminate on the basis of gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race. LI-HYBRID AI-First Mindset at Glean: At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required. Global Data Privacy Notice for Job Candidates and Applicants: Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy . By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement . By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement , and I agree to the terms.
AI Data Analyst
About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. About the Role: Glean is seeking an AI Data Analyst to help power the human evaluation system behind Glean’s AI product quality. This role sits at the centre of response quality, competitive benchmarking, and release readiness. You will evaluate AI outputs, label and categorize failure modes, maintain high-quality benchmark data, and help ensure the datasets and evals we rely on are realistic, consistent, and tied to measurable product quality lift. This is a highly cross-functional role. You will work closely with QA, Engineering, Product, and the evals team to convert ambiguous quality issues into structured data, actionable feedback, and repeatable quality signals. You will: - Run ongoing human labeling for priority AI workflows, including response quality, task success, MCP and tool use, end-to-end Cowork-style workflows. - Triage bad-query reports, downvotes, escalations, and routed quality issues; categorize failure modes and feed clean analysis back to partner teams. - Perform qualitative analysis to identify recurring patterns such as hallucinations, retrieval failures, tool-use issues, weak grounding, and poor workflow completion. - Follow and improve labeling guidelines and rubrics so judgments are consistent, realistic, and useful for model and product improvement. - Maintain high-quality labeled datasets, golden sets, and regression slices; monitor coverage, drift, leakage, and difficulty distribution. - Participate in calibration exercises with human graders and validate LLM-as-a-judge outputs against human labels to improve consistency and judge quality. - Partner with engineers, PMs, QA, and eval owners on pre/post-change quality reads, benchmark updates, and release-readiness decisions. - Contribute to recurring quality reporting by surfacing top failure modes, coverage gaps, quality shifts, and actionable recommendations. About you: - 3–5 years of experience in data labeling, data analysis, QA, or a related field. - Strong analytical judgment and attention to detail, with the ability to apply nuanced rubrics consistently. - Experience evaluating AI-generated outputs or working with NLP, search, recommendation, or other ML systems. - Familiarity with structured qualitative analysis, basic SQL/data retrieval, and spreadsheet-based workflows. - Clear written and verbal communication skills, with the ability to explain findings to both technical and non-technical audiences. - Ability to work independently in ambiguous, fast-moving environments and collaborate effectively across functions. Location: This role is hybrid (4 days a week in our offices). Compensation & Benefits: Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. We are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organization. We're committed to an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race. LI-HYBRID AI-First Mindset at Glean: At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required. Global Data Privacy Notice for Job Candidates and Applicants: Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy . By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement . By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement , and I agree to the terms.
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