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Research Engineer, Infrastructure, Kernels
ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We’re looking for an infrastructure research engineer to design, optimize, and maintain the compute foundations that power large-scale language model training. You will develop high-performance ML kernels (e.g., CUDA, CuTe, Triton), enable efficient low-precision arithmetic, and improve the distributed compute stack that makes training large models possible. This role is perfect for an engineer who enjoys working close to the metal and across the research boundary. You’ll collaborate with researchers and systems architects to bridge algorithmic design with hardware efficiency. You’ll prototype new kernel implementations, profile performance across hardware generations, and help define the numerical and parallelism strategies that determine how we scale next-generation AI systems. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Design and implement custom ML kernels (e.g., CUDA, CuTe, Triton) for core LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for modern GPU and accelerator architectures. - Design and think through compute primitives to reduce memory bandwidth bottlenecks and improve kernel compute efficiency. - Collaborate with research teams to align kernel-level optimizations with model architecture and algorithmic goals. - Develop and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training. - Contribute to infrastructure stability and scalability, ensuring reproducibility, consistency across precision formats, and high utilization of compute resources. - Document and share insights through internal talks, technical papers, or open-source contributions to strengthen the broader ML systems community. SKILLS AND QUALIFICATIONS Minimum qualifications: - Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar. - Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases - Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures. - Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts. - A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships. - Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks. - Demonstrated ability to analyze, profile, and optimize compute-intensive workloads. Preferred qualifications — we encourage you to apply if you meet some but not all of these: - Experience training or supporting large-scale language models with tens of billions of parameters or more. - Track record of improving research productivity through infrastructure design or process improvements. - Experience developing or tuning kernels for deep learning frameworks such as PyTorch, JAX, or custom accelerators. - Familiarity with tensor parallelism, pipeline parallelism, or distributed data processing frameworks. - Experience implementing low-precision formats (FP8, INT8, block floating point) or contributing to related compiler stacks (e.g., XLA, TVM). - Contributions to open-source GPU, ML systems, or compiler optimization projects. - Prior research or engineering experience in numerical optimization, communication-efficient training, or scalable AI infrastructure. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Research Scientist, Professional Creative Workflow...
Research Scientist, Professional Creative Workflows About the Role We are seeking a highly motivated Research Scientist to join our Research team. The role focuses on advancing the next generation of generative AI systems for professional creative workflows , spanning image, video, 3D, vector graphics, and design. This is a general research role with room for academic publication, long-horizon exploration, and technically ambitious work, while remaining grounded in real projects with meaningful product impact. The ideal candidate can move fluidly between frontier research, rigorous experimentation, and prototype systems that inform how professional creators work with generative AI. We are looking for a broad, technically deep researcher who can combine ideas across fields and modalities, connecting concepts from areas such as computer vision, graphics, generative modeling, and design to address complex creative workflows. You will work on research that enables creators and enterprises to generate, edit, structure, refine, and reuse visual assets across modalities, supporting workflows in which different forms of media and design intent work together rather than as isolated generation tasks. Responsibilities - Conduct original research in generative AI for professional creative workflows across image, video, 3D, vector graphics, and design. - Develop new methods that advance the state of the art while remaining relevant to real creative and production workflows. - Publish research at leading academic venues where appropriate, while also contributing to high-impact internal projects. - Explore multimodal generative systems that integrate image, video, 3D, vector graphics, and design, with an emphasis on controllability, editability, and reusable asset creation. - Translate research insights into prototypes, experiments, and model capabilities that can inform product direction. - Work with researchers, engineers, designers, and product teams to identify important research problems with meaningful downstream impact. - Design datasets, benchmarks, and evaluation protocols for professional workflows where quality, editability, consistency, brand adherence, and production usefulness matter. - Stay at the frontier of generative media research and identify opportunities to combine ideas across computer graphics, vision, language, video, and creative tooling. - Contribute to a research culture that values technical depth, intellectual breadth, publication quality, and practical relevance. Qualifications - Strong academic research track record in computer vision, computer graphics, machine learning, generative modeling, computational imaging, visual computing, or a related field. - PhD or equivalent research experience in Computer Science, Machine Learning, Computer Graphics, Computer Vision, or a closely related discipline. - Demonstrated ability to conduct original research, publish papers, and build working systems or prototypes. - Experience in generative and multimodal modeling, including areas such as diffusion models, transformers, 3D generation, neural rendering, inverse rendering, video generation, image editing, controllable generation, or design automation. - Broad technical background across several of the following areas: - Generative image models - Video generation or video editing - 3D asset generation, reconstruction, neural rendering, or geometry processing - Differentiable rendering, inverse rendering, materials, lighting, or relighting - Vector graphics, layout, typography, or design systems - Multimodal representation learning - Professional media, VFX, games, e-commerce, design, or creative tooling - Strong programming skills in Python and PyTorch, with the ability to implement, adapt, and extend modern research code. - Ability to reason about both research quality and workflow quality, including controllability, iteration speed, editability, consistency, reliability, and integration into existing creative processes. - Strong communication skills, with the ability to explain research ideas clearly to both research and product-oriented collaborators. - Evidence of creative technical judgment: the ability to combine ideas from different fields into new research directions rather than only incrementally improving existing systems. Highly Desirable - Publications at top-tier venues such as CVPR, ICCV, ECCV, SIGGRAPH, SIGGRAPH Asia, NeurIPS, ICLR, ICML, or related venues. - Research experience spanning more than one modality, especially across image, video, 3D, vector graphics, design, or multimodal generation. - Experience with professional creative domains such as VFX, games, animation, product visualization, advertising, graphic design, or 3D asset production. - Experience with controllable generation, layered image editing, material and lighting decomposition, multi-view consistency, relightable assets, vector-aware generation, or structured design outputs. - Experience building research prototypes that influence product direction or enable new product capabilities. Equal Employment Opportunity We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability, or other legally protected statuses.
Senior | Staff Software Engineer - AI / ML
About Snorkel Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! In September 2026 we raised a $350 million Series E at a $3.5 billion valuation , and we are scaling our engineering and research teams to meet demand. The role Frontier AI data is expensive to make and hard to measure. Every task we deliver is tested against the strongest models, often through many long-running agent rollouts. Your job is to make that process faster, cheaper, and more rigorous with ML and AI You will be one of the early members of ML & Research Engineering at Snorkel. You will study how frontier-grade data is generated and evaluated, form hypotheses, validate them against real production data, and ship the winners at scale. You will shape the discipline's direction, its standards, and the team that grows around it. What you'll work on - Efficient agentic evals. Cut the cost of long-horizon agent evaluation with adaptive sampling, statistically grounded early stopping, model cascades, caching, and cheap-first gating. - AI model routing. Route every eval and judge call to the cheapest model that clears the quality bar, with fallback, monitoring, and cost attribution. - Fine-tuned small models. Fine-tune and serve open-weight models (LoRA and other parameter-efficient methods) where they match frontier quality, and know when they don't. - Predictive difficulty. Build models that estimate how hard a task is for frontier systems before running a single rollout. - Measurement for AI data. Build golden datasets, quantify the accuracy and calibration of LLM-as-judge systems, and make quality reproducible across projects. - Research to production. Turn research prototypes into reusable, configurable components that forward deployed engineers and researchers use on every project. What you'll bring - 5+ years building production ML or software systems, with end-to-end ownership from prototype to production - Hands-on experience running LLM or ML workloads in production, and comfort reasoning about non-deterministic systems - Deep grounding in statistics and experimentation: experiment design, hypothesis testing, sampling, and confidence intervals - Strong Python and software engineering fundamentals, including testing, code review, and system design - Experience designing evaluations and interpreting results rigorously - A habit of finding high-impact problems before they are assigned, and clear communication with researchers, engineers, and business partners Nice to have - Fine-tuning and serving open-weight models, and judging when a smaller model meets the quality bar - Building LLM evaluation or experimentation platforms, model gateways, or routing systems - Experience with agentic workloads, benchmarks, or RL environments - A record of taking research into production: publications, open-source work, or shipped research-driven features - MS or PhD in Computer Science, Machine Learning, Statistics, or a related field Why join now - Frontier problems. Measure and shape the tasks designed to challenge the strongest models in the world. - All AI, no plumbing. Every problem on this team is an open ML or LLM problem. - Founding impact. Help define what ML engineering means at Snorkel and grow the team that carries it forward. - Visible results. Your work shows up directly in the speed, quality, and cost of the data frontier AI is built on. Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location. Salary range(s) for this role $208,000 - $315,000 USD Be Your Best at Snorkel Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success. Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. All employment is decided on the basis of qualifications, performance, merit, and business need. 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.
Senior Staff Engineer, ML Ops (R4941)
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. Job Description: Shield AI builds autonomy systems for defense applications, including air, maritime, and space platforms operating in complex and contested environments. We are building the AI Factory Reference Architecture, a Kubernetes-native platform for developing, training, evaluating, and deploying next-generation AI systems. The AI Factory serves two purposes. Internally, it powers autonomy development across Hivemind and other AI programs. Externally, it becomes the reference architecture deployed into customer environments, spanning commercial cloud, on-premise infrastructure, sovereign deployments, and fully air-gapped systems. We are looking for a Senior Staff Engineer to help define and build this platform. You will partner closely with ML researchers, platform engineers, and autonomy teams to deliver an exceptional developer experience for training and deploying modern AI models. Success in this role requires balancing researcher productivity, platform simplicity, operational excellence, and long-term maintainability. You will work hands-on across the stack, helping shape both the platform architecture and its implementation while staying closely aligned with the rapidly evolving AI ecosystem. What you'll do: AI Platform Development: Lead the design and implementation of the AI Factory Reference Architecture, delivering a Kubernetes-native platform for AI development, distributed training, simulation, evaluation, and deployment. AI Research Enablement: Partner directly with ML researchers to understand evolving training workflows and ensure the platform supports state-of-the-art AI frameworks, foundation model development, reinforcement learning, distributed training, and emerging research workflows. Developer Experience: Design self-service AI development workflows that enable engineers to move seamlessly from local experimentation to large-scale distributed execution using familiar open source tools and frameworks. Distributed AI Infrastructure: Build the infrastructure required to support distributed training, simulation, inference, and reinforcement learning workloads. Evaluate and integrate orchestration, scheduling, and resource management technologies to maximize scalability and developer productivity. Compute Platform: Design and optimize shared GPU infrastructure across cloud and on-premises environments. Improve resource utilization, scheduling efficiency, storage, networking, observability, and overall platform reliability. Data & Model Lifecycle: Build platform capabilities that enable dataset management, experiment tracking, artifact management, model versioning, evaluation, deployment, monitoring, and continuous model improvement. Platform Distribution: Develop repeatable deployment and lifecycle management solutions using Infrastructure as Code and modern platform engineering practices. Support commercial cloud, customer-managed infrastructure, sovereign environments, and fully air-gapped deployments. Technology Leadership: Evaluate emerging AI infrastructure technologies and establish architectural patterns that balance scalability, performance, maintainability, and developer experience. Cross-Functional Collaboration: Work closely with AI researchers, autonomy teams, infrastructure engineers, and product teams to ensure the platform evolves alongside customer needs and advances in AI. Key Outcomes: Engineers move from idea to distributed training in hours instead of days. High GPU utilization through efficient scheduling with KAI on Kubernetes. Researchers use modern AI tooling without unnecessary platform friction. AI Workspaces become the standard development environment across autonomy programs. Training, simulation, evaluation, and deployment operate as a unified platform. The AI Factory Reference Architecture can be deployed consistently across cloud, on-premise, and air-gapped environments. Platform capabilities are reusable across multiple autonomy programs and customer deployments. Required qualifications: Experience building Kubernetes-native AI or MLOps platforms supporting distributed machine learning workloads. Deep understanding of modern AI training frameworks, including PyTorch, Hugging Face Transformers and distributed training techniques. Experience operating GPU-accelerated infrastructure and distributed training systems. Strong understanding of Kubernetes, Linux, networking, security, storage, and distributed systems. Experience with GPU scheduling concepts and large-scale AI workloads. Experience packaging and deploying cloud-native infrastructure using Terraform and Helm. Strong software engineering skills in Python and Golang and modern cloud-native technologies. Experience collaborating closely with ML researchers to translate research workflows into scalable platform capabilities. Preferred qualifications: Experience with Ray or other distributed AI orchestration frameworks. Experience with KAI, Slurm or other GPU scheduling technologies. Experience supporting reinforcement learning, simulation-driven training, robotics, or autonomy workloads. Experience deploying and optimizing AI models for edge hardware. Experience designing infrastructure for classified, sovereign, or air-gapped environments. Experience with observability technologies such as OpenTelemetry, Prometheus, and Grafana. Experience contributing to or maintaining open-source infrastructure projects. Why Join Us The AI Factory is the foundation for how Shield AI develops and deploys autonomy. As a Senior Staff Engineer, you will help build the reference architecture used internally by our engineering teams and delivered to customers building their own AI capabilities. Your work will directly impact how quickly researchers can iterate, how efficiently compute resources are utilized, and how AI systems move from experimentation to operational deployment. This is an opportunity to work at the intersection of modern AI, distributed systems, Kubernetes, and defense, building a platform that enables the next generation of autonomy systems while leveraging the best of the open-source AI ecosystem.
Research Scientist
About Sesame Sesame believes in a future where computers are lifelike - with the ability to see, hear, and collaborate with us in ways that feel natural and human. With this vision, we're designing a new kind of computer, focused on making voice agents part of our daily lives. Our team brings together founders from Oculus and Ubiquity6, alongside proven leaders from Meta, Google, and Apple, with deep expertise spanning hardware and software. Join us in shaping a future where computers truly come alive. ABOUT THE ROLE New and novel approaches are needed to realize all of our product goals. As a Machine Learning Scientist at Sesame, you are a research-oriented person with experience in NLP, Speech, and/or Computer Vision with a focus on deep learning. You are up to date on recent papers and approaches and you have the creativity and the intuition to come up with new solutions based on the application at hand. RESPONSIBILITIES: - Contribute to the development of our ML models across multiple modalities. - Work across the ML stack, including model architectures, data curation, model evaluation, training & inference infrastructure, research, and experimentation. - Pick promising approaches from the literature to bet on, and create new approaches where necessary to achieve our unique goals. REQUIRED QUALIFICATIONS: - Experience working independently in high-ambiguity environments. - Published papers in NLP, Speech, or Computer Vision involving large-scale deep learning. - Familiar with the state of the art in AI. - Bachelor’s degree or higher in CS or related field. PREFERRED QUALIFICATIONS: - Masters/PhD desired. - Experience working with products. - Experience in a startup environment. Sesame is committed to a workplace where everyone feels valued, respected, and empowered. We welcome all qualified applicants, embracing diversity in race, gender, identity, orientation, ability, and more. We provide reasonable accommodations for applicants with disabilities. Contact careers@sesame.com for assistance. Full-time Employee Benefits: - 401 (k) max employer match: 3.5% of compensation - 100% employer-paid health, vision, and dental benefits for you and your dependents - Unlimited PTO and sick time - Flexible spending account with employer matching up to $1,650/year (medical FSA) - Guardian Employee Assistance Program (EAP) - Opportunity to share in the company's success with competitive stock options Benefits do not apply to contingent/contract workers.
AI Agent Security Architect
Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the Role We are looking for an AI Agent Security Architect to function as the primary technical authority for Replit’s autonomous and AI agent security blueprint. In this critical role, you will design, implement, and maintain the runtime defense systems, guardrail frameworks, and sandboxing architectures that govern AI agents executing code, invoking tools, and reasoning across our platform. You will be a key technical contributor—leading high-impact AI security initiatives and bridging the gap between non-deterministic AI behavior and rigorous cybersecurity controls for both engineering and executive leadership. What You'll Do - AI Agent Security Strategy & Technical Execution - AI Agent Security Blueprint: Define the long-term vision and architectural patterns for securing autonomous agent workflows, Model Context Protocol (MCP) integrations, multi-turn reasoning loops, and multi-agent coordination. - Runtime Guardrails & Policy Enforcement: Architect and deploy dynamic input/output guardrail systems, semantic firewalls, and real-time intent verification filters to prevent goal hijacking, system prompt leaks, and indirect prompt injections. - Agent Execution & Tool Sandboxing: Partner with Infrastructure and AppSec teams to design secure, short-lived, micro-isolated environments (e.g., microVMs, WebAssembly, container sandboxes) where agents can dynamically execute code, run shell commands, and interact with host operating systems safely. - Agentic Threat Modeling & Red Teaming: Conduct specialized threat modeling against non-deterministic systems. Lead automated and manual AI red-teaming initiatives to uncover vulnerabilities in RAG context pipelines, vector stores, and tool-calling interfaces. - Identity, Delegation & Least Privilege: Architect fine-grained permission models and step-up Human-in-the-Loop (HITL) authorization patterns for agents acting on behalf of users—ensuring agents never exceed the delegated privileges of the end user or misuse API keys. - Context & Memory Boundary Security: Design strict data isolation and poisoning prevention controls for vector databases, retrieval-augmented generation (RAG) pipelines, and long-term conversation memories across multi-tenant workloads. - Risk Management & Cross-Functional Enablement - Maintain the AI Security Source of Truth: Define, document, and maintain authoritative secure design patterns and SDKs for engineering teams building internal or customer-facing AI agent capabilities. - Contribution to Risk Register: Identify, quantify, and document non-deterministic and agentic security risks (e.g., OWASP Top 10 for LLMs & AI Agents, MITRE ATLAS), ensuring they are accurately represented in the Cybersecurity Risk Register. - Auditability & Observability: Design tamper-evident logging and telemetry standards for agent reasoning chains, tool execution history, and context assembly to support incident response, forensics, and GRC requirements. - Compliance & Sales Enablement: Partner with GRC to translate complex AI security controls into enterprise-ready audit documentation (e.g., ISO 42001, NIST AI RMF, EU AI Act readiness) and support Sales on complex enterprise security inquiries regarding AI safety. Required Skills & Experience - 6+ years of experience in security engineering or security architecture, with at least 2+ years dedicated specifically to AI/ML security, LLM application security, or securing agentic frameworks. - Deep understanding of agentic architectures and protocols (e.g., Model Context. Protocol (MCP), LangChain, AutoGen, CrewAI, ReAct frameworks, and vector database technologies). - Hands-on expertise with threat vectors unique to agentic AI: Indirect Prompt - Injection, Goal Hijacking, Tool Parameter Tampering, Data Poisoning, and Context Contamination. - Strong background in applying safety standards and risk frameworks such as OWASP Top 10 for LLMs & AI Agents, NIST AI RMF, and MITRE ATLAS. - Proficiency in Python, Go, or TypeScript/JavaScript with a proven track record of reviewing code and building functional security tooling or guardrails. - Experience authoring, maintaining, and evangelizing technical security architecture documentation. - Exceptional ability to communicate complex non-deterministic AI risks to both deep technical engineers and executive stakeholders. - Proven track record of contributing to an enterprise Cybersecurity Risk Register. Bonus Qualifications - Experience designing runtime sandboxing and isolation technologies (e.g., Firecracker, gVisor, Docker, WebAssembly) for dynamic code execution environments. Full-Time Employee Benefits Include: 💰 Competitive Salary & Equity 💹 401(k) Program with a 4% match (US Only) ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🏝 Flexible Time Off (FTO) + Holidays 🚗 Commuter Benefits (In-Office & US Only) 📱 Monthly Wellness Stipend 🧑💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement (In-Office Only) 🚀 Quarterly Team Gatherings ☕ In Office Amenities (In-Office Only) Want to learn more about what we are up to? - Self-driving Company https://replit.com/blog/self-driving-company - Replit Agent at Scale https://replit.com/blog/evaluating-and-improving-agent-at-scale - AI Adoption https://replit.com/blog/ai-adoption - Build Open-Source Apps https://replit.com/build/open-source-app-builder Interviewing + Culture at Replit - Operating Principles https://blog.replit.com/operating-principles - Reasons not to work at Replit https://blog.replit.com/reasons-not-to-join-replit To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.
Member of Technical Staff (ML)
As a Member of Technical Staff on Machine Learning, you will: - Contribute to the entire development cycle of our cutting-edge large deep learning models. - Prepare datasets, design architectures, implement solutions, train and evaluate models to improve our products. - Collaborate closely with engineers and researchers to translate cutting-edge research into practical applications. - Join us at an exciting moment, wear many hats and help us build something from the ground up! You may be a good fit, if you have: - Experience with training and evaluating large deep learning models. - Proficiency in standard deep learning frameworks (e.g. PyTorch, JAX). - Proven track record of applying and implementing machine learning algorithms into software applications at scale. - The ability to build in a fast-paced environment under some uncertainty. - A kind and collaborative nature and work style. Reka's Mission Reka's mission is to build useful multimodal artificial intelligence and use it to empower organisations and businesses. We are a globally distributed foundation model startup, headquartered in the San Francisco Bay Area, California. Embracing a remote-first approach, our team brings together top talent from around the world. Our founding team, along with many of our team members, has contributed to many of the breakthroughs in AI over the past decade. Why Reka? - An Elite Team: Collaborate with top-tier engineers, researchers, operators from renowned organizations like Google DeepMind and Facebook AI Research (FAIR) and successful startups, driving innovation in cutting-edge AI technology. - Massive Market Opportunity: Be part of a rapidly growing industry poised to transform multiple sectors globally, offering the chance to make a significant impact. - Mission-Driven Environment: Work alongside a collaborative, mission-focused team dedicated to advancing AI for meaningful applications. - Inclusive and Open Culture: Thrive in an open and inclusive work environment that values diverse perspectives and fosters creativity. - Generous Benefits: Enjoy 5 weeks of paid leave to recharge, comprehensive healthcare benefits including vision and dental, and additional perks that support your well-being. - Visa Support: We provide visa assistance, including H1B and OPT transfers, for US employees to ensure a smooth transition and support your career with us.
Forward Deployed Engineer, Lead - LLM Post-trainin...
OUR MISSION Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all. Role Overview We're seeking an exceptional technical leader to build and scale Reflection's post-training and evaluation capabilities within the Applied AI team. This team works at the intersection of model adaptation, sovereign deployment, and enterprise deployment: taking Reflection's open-weight models and making them work for specific customer domains, tasks, and constraints. As a Forward Deployed Engineer Lead, Post-Training, you will own the end-to-end technical strategy for model customization, from synthetic data generation and reward modeling through training and production deployment. You will work directly with customers to understand their needs and with research teams to push what's possible with our models. What You'll Do - Lead post-training engagements with enterprise customers: assess their data, define training strategies, design reward signals and verifiers, prepare datasets, run training loops, and evaluate results against customer-specific benchmarks. - Design and build RL training environments for model adaptation, including synthetic data generation pipelines, reward model training, and preference data collection workflows. - Design and build evaluation infrastructure: define what "better" means for each customer use case, build eval harnesses, curate test sets, and establish baselines that measure real-world performance. - Own the data pipeline from raw customer data through training-ready datasets, including synthetic data generation, data quality inspection, cleaning, and format standardization. - Deploy post-trained models across hybrid environments (public cloud, VPC, and on-premises), working with infrastructure teams to ensure inference performance, cost efficiency, and reliability at scale. - Shape and scale the post-training and evaluation practice by defining playbooks, best practices, and technical standards. Mentor engineers on the team and help define what great applied AI work looks like at Reflection. What We're Looking For - Hands-on post-training experience with large language models at scale. You have built and operated RL training environments, designed preference optimization workflows on models at 50B+ parameter scale, and shipped the results to production. - Experience building synthetic data generation pipelines, reward models, and verifiers for reinforcement learning workflows. You've architected the data and feedback loops that make post-training work. - Deep understanding of evaluation methodology: how to design evaluations that measure what matters, how to interpret training dynamics, and how to tell the difference between a model that looks good on a benchmark and one that actually works. - Practical experience with training infrastructure at scale: comfortable working with multi-node GPU clusters, managing large training runs, debugging distributed training, and optimizing for cost. - Strong software engineering fundamentals. You write production-quality code, not just notebooks. Experience with data pipelines, version control for datasets and models, and reproducible workflows. - 6+ years of engineering experience, including 2+ years focused on LLM post-training in a leadership capacity (e.g., Tech Lead on a post-training team, Senior MLE owning preference optimization for a product, or Lead Applied Scientist running RL training pipelines in production). - Experience in customer-facing technical roles, or a genuine interest in developing this skill. In either case, you are comfortable translating domain requirements into training strategies and delivering measurable outcomes. - Self-starter with high agency and ownership, excelling in fast-paced startup environments where playbooks are still being written. WHAT WE OFFER: We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models. We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported. - Top-tier compensation: Salary and equity structured to recognize and retain our talent globally. - Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options. - Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance. - Meals: Lunch and dinner are provided in the office daily. - Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys. - Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K. - Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable. - Team building: We have regular off-sites, happy hours, and team celebrations. Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization. Candidate and Employee Privacy Notice: We collect and process personal data about applicants for the purposes described in our Candidate and Employee Privacy Notice https://cdn.sanity.io/files/irpbdun9/production/ee08c38bf22bd45a3f3a81f6a7abddb9210726b7.pdf
Research Scientist, Post-Training — Video Generati...
ABOUT THE ROLE At Pika, we are pioneering the next generation of creative infrastructure built around real-time, multimodal generation and intelligent agentic platforms. We are seeking Research Scientists with expertise in RL post-training and generative modeling for large-scale video generation. The focus is on refining Pika's video generation models using RL alignment and building robust video reward models. This is a staff and lead-level opportunity. As a key member of our research team, you will own RL-based post-training for video diffusion/flow-matching models, develop state-of-the-art reward models, and lead post-training evaluation across human and automated metrics. You will collaborate closely with engineering and product teams, shaping the frontier of real-time creative and agentic video platforms. SCOPE - RL alignment of Pika's video generation models and the reward models that drive them. - Distillation of RL-tuned models is a secondary focus. RESPONSIBILITIES - Run RL post-training (preference optimization, online RL against learned rewards) for video diffusion/flow-matching models at multi-node scale. - Build video reward models: define target evaluation dimensions, design/configure preference data collection workflows, train and validate learned judges, and safeguard against reward hacking. - Own post-training evaluation, including human preference studies and their correlation with automated metrics. - Distill RL-tuned models to efficient few-step samplers while preserving alignment gains (secondary focus). WHAT WE’RE LOOKING FOR REQUIRED - 2+ years hands-on research experience in post-training or generative modeling. - RL or preference-optimization experience on generative models with evidence of model improvement. - Strong grounding in diffusion or flow-matching models, PyTorch, and multi-node distributed training. PREFERRED - Experience developing reward models for visual generation, including VLM-as-judge or large-scale preference data collection. - Distillation expertise (distribution matching, consistency, adversarial approaches), ideally for video models. - Familiarity with video-specific failure modes: temporal drift, motion and physics realism. WHAT WE OFFER - Competitive salary and substantial equity in a high-growth startup - Full health benefits + 401k matching and more - Collaborative, mission-driven team environment with significant growth opportunities - Flexible on-site/remote hybrid (HQ in Palo Alto, CA) ABOUT PIKA Pika empowers creators by building state-of-the-art agentic and multimedia platforms. Our vision is to break down technical barriers to creativity, making real-time generative and intelligent orchestration accessible to all. Join us to shape the next evolution of creative technology! If you are passionate about advancing RL alignment and generative modeling for video, and want to scale real-time multimodal foundation models, we want to hear from you.
Senior Applied Scientist, Speech
The Opportunity Do you want to lead projects to build and deploy cutting-edge AI technology to help people get unparalleled value from meetings and conversations? Join our core AI team responsible for ML and work alongside industry-veteran scientists and engineers. As a Senior Applied Scientist, you’ll bring your strong software engineering mindset to machine learning in order to scale and optimize our speech systems—creating and transforming innovative research into production-ready features that power Otter’s summarization and conversational intelligence products. Your Impact - Architect, build, and evolve large-scale ASR and TTS systems that power mission-critical product experiences including speech understanding across millions of conversations. - Lead the design and implementation of training, fine-tuning, post-training, and inference strategies for speech models using PyTorch making principled trade-offs across quality, latency, cost, and reliability. - Design and improve model architectures, loss functions, decoding strategies, and training techniques for speech models, informed by both research and production constraints. - Own end-to-end ML system lifecycles , from research prototyping through production deployment, monitoring, iteration, and long-term maintenance. - Partner deeply with product, and infrastructure teams to develop and translate cutting-edge research into scalable, production-grade systems that deliver measurable user and business impact. - Drive system-level improvements in model performance, robustness, observability, and operational excellence using real-world conversational data at scale. - Set technical direction and best practices for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows in a cloud environment. - Identify and resolve complex, ambiguous problems in model behavior, data quality, scaling, and system interactions, often before they surface as user-visible issues. - Mentor and elevate other engineers , influencing team standards, reviewing designs, and contributing to a culture of strong technical decision-making and execution. We're Looking for Someone Who - Holds a Bachelor’s or Master’s degree in Computer Science or a related field with 5+ years of relevant industry experience; PhD is preferred. - Has deep, hands-on experience building and fine-tuning speech or foundation models, with production experience in ASR and/or TTS systems. - Demonstrates strong command of modern ML research, with the ability to critically evaluate new papers and drive innovation by identifying what is production-worthy versus experimental. - Has experience deploying, scaling, monitoring, and operating ML systems in production across training, inference, and serving infrastructure. - Is comfortable working with large-scale speech and conversational datasets, including data preprocessing, augmentation, quality analysis, and labeling strategies to support model training and evaluation. - Can lead technical projects independently, driving clarity in ambiguous problem spaces and making sound architectural decisions. - Has experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration. About Otter.ai We are in the business of shaping the future of work. Our mission is to make conversations more valuable. With over 1B meetings transcribed, Otter.ai is the world’s leading tool for meeting transcription, summarization, and collaboration. Using artificial intelligence, Otter generates real-time automated meeting notes, summaries, and other insights from in-person and virtual meetings - turning meetings into accessible, collaborative, and actionable data that can be shared across teams and organizations. The company is backed by early investors in Google, DeepMind, Zoom, and Tesla. Otter.ai is an equal opportunity employer. We proudly celebrate diversity and are dedicated to inclusivity. *Otter.ai does not accept unsolicited resumes from 3rd party recruitment agencies without a written agreement in place for permanent placements. Any resume or other candidate information submitted outside of established candidate submission guidelines (including through our website or via email to any Otter.ai employee) and without a written agreement otherwise will be deemed to be our sole property, and no fee will be paid should we hire the candidate. Salary range Salary Range: $230,000 to $265,000 USD per year. This salary range represents the low and high end of the estimated salary range for this position. The actual base salary offered for the role is dependent based on several factors. Our base salary is just one component of our comprehensive total rewards package. LI-Hybrid
Senior System Software Engineer - Local AI
NVIDIA has continuously reinvented itself for more than two decades. The invention of the GPU in 1999 fueled the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. GPU-powered deep learning helped ignite the era of modern AI, establishing GPUs as the foundation of intelligent applications across productivity, gaming, and creative workflows, and reinforcing NVIDIA's position as a leading AI computing company. More recently, there is a growing focus on running AI models locally, closer to where data is generated. This approach reduces latency, enables real-time processing, and addresses privacy concerns by minimizing the need to send data to centralized servers. As technology continues to evolve, client-side AI will play an increasingly important role in shaping the digital landscape. The LocalAI team is seeking a Senior Systems Software Engineer to lead the architecture of efficient on-device AI software for RTX and DGX-class systems. The role focuses on delivering high-performance local inference with low latency, optimized memory utilization, robust infrastructure, and practical deployment on resource-constrained platforms What You'll Be Doing: Partner with NVIDIA's software, research, architecture, and product leadership, as well as external partners such as Microsoft, to align technical requirements and strategic priorities, fostering the AI ecosystem on RTX and DGX PCs. Build and optimize the local AI inference stack for RTX, RTX Pro, and DGX GPUs, with a focus on performance, stability, and scalability across diverse hardware architectures. Architect and develop modern inference runtimes and execution stacks using frameworks such as llama.cpp, vLLM, PyTorch, WinML, DXCGC, and TensorRT-RTX, supporting LLM, vision-language, TTS, ASR, and diffusion-based AI workloads. Perform end-to-end optimization of AI models, data pipelines, and inference runtimes to maximize performance on current and next-generation GPU architectures. Apply model optimization techniques, including quantization, pruning, sparsity, and distillation, to enable efficient deployment of large models on local and edge devices. Lead system-level debugging, performance tuning, and performance-accuracy trade-off analysis; develop infrastructure for performance and accuracy sweeps; analyse results to identify gaps and drive fixes; and establish engineering guidelines to accelerate bring-up and ensure production readiness of new models and inference backends. Mentor other engineers and review architecture proposals spanning several groups, acting as a force multiplier for technical quality and consistency across the broader LocalAI organization. What we need to see: 5+ Years of experience with Bachelor’s, Master’s, or PhD in Computer Science, Software Engineering, Mathematics, or related field, or equivalent experience. Excellent C++ programming and debugging skills, with a strong foundation in data structures, algorithms, and machine learning. Proven experience architecting and optimizing AI inference pipelines and applications using ML/DL frameworks such as Llama.cpp, vLLM, PyTorch, Windows ML, DXCGC, and TensorRT. Deep understanding of inference backends and runtime internals, including scheduling, memory management, KV-cache behavior, graph execution, quantization, and hardware-aware optimization. Demonstrated ability to establish technical direction and influence outcomes across multiple teams and organizations without relying on formal management authority. Strong analytical and problem-solving skills, with the ability to manage multiple priorities effectively in a fast-paced environment. Excellent written and verbal communication skills, enabling effective collaboration across engineering teams and management. Ways to stand out from the crowd: Deep understanding of modern machine learning, deep neural network, and generative AI techniques, with exposure to AOT graph-compilation systems (TensorRT builder, MLIR-based compilers) Consistent track record of delivering end-to-end products in multinational companies with geographically distributed teams. Expert-level low-level systems and GPU kernel programming, CUDA, and development of high-performance systems. Significant contributions to open-source inference runtimes, model tooling, or performance infrastructure, as a maintainer or core contributor. Hands-on experience building applications using frameworks and APIs such as llama.cpp, PyTorch, TensorRT, Vulkan, DirectX, and vLLM. We're a top employer recognized for innovation, growth, and commitment to diversity as an equal opportunity workplace. We offer competitive salaries, a generous benefits package, and the opportunity to work alongside some of the industry's most talented and forward-thinking professionals. As our engineering teams continue to grow rapidly, we're looking for creative, self-driven engineers with a passion for technology to join us.
Senior Manager, Machine Learning Ops Engineering -...
NVIDIA is seeking a Senior MLOps Engineering Manager to join our Autonomous Driving organization in Santa Clara, CA. This role offers an outstanding opportunity to lead the build, development, and operation of large‑scale, end‑to‑end data and ML pipelines that power NVIDIA’s autonomous driving products. You will lead a highly technical engineering team responsible for building and operating cloud‑scale pipelines that ingest, validate, process, and transform extensive volumes of multimodal sensor data—including camera, lidar, and radar—into high‑quality training, evaluation, and validation datasets. These pipelines are foundational to NVIDIA’s AV program and directly enable customer‑facing autonomy features. We want a seasoned engineering leader with strong ownership and passion for customer‑focused development. This person will scale systems and teams in a fast paced, multi‑functional environment. What You Will Be Doing: Lead and grow a high‑performing MLOps engineering group tasked with managing end‑to‑end data pipelines supporting NVIDIA’s autonomous driving technology from levels L2 through L4. Own the architecture, execution, and operational excellence of large‑scale, cloud‑native pipelines for multimodal sensor data ingestion, processing, labeling, and validation. Drive the development of robust, scalable, and observable MLOps systems that support model training, ground truth generation, and continuous evaluation at AV scale. Partner closely with perception, ML, data labeling, infrastructure, and product teams to translate customer and program requirements into reliable production systems. Define technical vision, roadmap, success metrics, and operational benchmarks, and ensure consistent execution against program achievements. Champion customer‑first thinking and ownership, ensuring the systems your team builds directly deliver measurable value to internal and external AV customers. Balance hands‑on technical depth with people leadership, providing technical guidance, mentorship, and career development for senior engineers and managers. Operate across multiple layers of the stack, including Python, C++, distributed systems, cloud infrastructure, CI/CD, and data platforms. What We Need to See: Bachelor’s or equivalent experience, Master’s, or PhD in Computer Science, Electrical Engineering, or a closely related field (or equivalent experience). 10+ overall years of overall engineering experience, including crafting and coordinating production‑grade distributed systems. 5+ years of engineering management experience, with a proven history of guiding teams delivering sophisticated, large‑scale systems. Strong background in MLOps, data pipelines, and cloud‑based distributed systems. Proficiency in Python and C++, with the ability to guide system‑level and performance‑critical build decisions. Experience crafting and operating end‑to‑end data or ML pipelines with high reliability, scale, and observability. Prior experience in one or more of the following domains: Autonomous Vehicles, Robotics, Computer Vision, Deep Learning, or GPU‑accelerated computing. Excellent communication and leadership skills, capable of aligning collaborators and driving execution in a multi-functional organization. Demonstrated passion for ownership, accountability, and engineering that prioritizes customers. Ways to Stand Out from the Crowd: Experience developing and leading AV‑scale data platforms handling petabyte‑scale sensor data. Strong background of leading teams responsible for production MLOps or data infrastructure. Experience with automotive or robotic systems, including real‑world sensor data pipelines. Background in distributed cloud systems, workflow orchestration, and large‑scale CI/CD. Familiarity with 3D geometry, perception pipelines, or data generation based on simulated environments. NVIDIA is widely considered one of the most sought-after employers in the technology industry. We offer highly competitive compensation along with an extensive benefits plan. Our work enables new universes of discovery—from artificial intelligence to autonomous vehicles—and brings once‑science‑fiction technologies into reality. If you are passionate about autonomous driving, scaling complex systems, and leading teams that deliver real customer impact, we would love to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 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.
Senior DFX Software Engineer - Machine Learning
We are now looking for a Senior DFX Software Engineer - Machine Learning. Do you like to think creatively and enjoy solving challenges that require innovation? If so, we may have an opportunity for you. On our team we define and build methodologies, software, and flows tailored to the field of silicon device testing, silicon debug, and silicon failure analysis. We owe our success to our people, some of the brightest in the world, and a company culture that fosters and encourages innovation, and fuels our creativity! Our team contributes to the advancement of all the fields in which NVIDIA participates, from gaming to building groundbreaking state-of-the-art compute platforms and Artificial Intelligence, by enabling high quality Silicon defect screening to sustain all these fields. This often requires new ways of thinking in order to meet new challenges, and we pride ourselves in our ability to tackle these challenges in ways that enable our success. If you are a like-minded person who enjoys innovation and likes to solve technical challenges then we would love to hear from you! What you'll be doing: Develop high performance software to enable design and development of efficient test pattern generation, application of these patterns on Silicon, failure analysis, and yield learning Create efficient parallel graph traversal and graph analysis techniques Work with multi-functional teams to assess and tackle problems that involve multiple areas of expertise through the company Apply LLMs, RAGs, graph-based ML approaches, and reinforcement learning to define innovative solutions What we need to see: BS in EE or CS (or equivalent experience); MS or higher degree preferred 5+ years of experience in Software development Strong programming experience in Python or C++. Hands on development in modern C++ is a huge plus. Experience use of LLMs (Large Language Models), GNNs (Graph Neural Networks), and Reinforcement Learning for efficient EDA solution. Expertise in high performance algorithms for DFT, simulations, and failure analysis. Understanding of different agent architectures, RAG systems, and communication protocols Deep familiarity with reinforcement learning algorithms like PPO, SAC, or Q-learning, including experience tuning hyper-parameters and reward functions Hands on experience with large scale training (e.g., ZeRO) and data processing (e.g. Spark) Excellent communication skills Ways to stand out from the crowd: Proven deployment of large-scale agentic application with high concurrency and agility Experience with software and hardware especially involving DFT, failure analysis, and CAD tools Working experience of agentic models / frameworks, observability and evaluation tools In-depth understanding of the graph neural networks, and reinforcement learning for logic design automation Experience with fine-tuning large language models, building advanced multi-agent systems, RAG pipelines and vector databases NVIDIA offers highly competitive salaries and a comprehensive benefits package. We have some of the most brilliant and talented people in the world working for us and, due to unprecedented growth, our world-class engineering teams are growing fast. If you're a creative and autonomous engineer with real passion for technology, 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 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 22, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Senior Context Fusion AI Engineer - Autonomous Veh...
We are looking for a strong engineer to join the DRIVE Road Structure / Online Mapping / Context Fusion team. In this role, you will help craft and guide the future of our L3/L4 autonomous-driving solution by building a complete, learned 3D/4D world model that fuses navigation, ego-motion, perception, and sensor signals. You will work closely with perception, prediction, planning, and simulation teams to deliver a world representation that is complete, temporally consistent, uncertainty-aware, and robust enough to drive through the most challenging roads and intersections in L3/L4 autonomy level. This role is central to our vision for AV: developing a shared multimodal scene representation that can jointly support road understanding, 3D object and occupancy perception, motion prediction, and route-conditioned planning. Are you interested in inventing human-level AI for navigation in the unconstrained world under any conditions? If so, join us ! What You'll Be Doing: Design and develop learning-based, multimodal sensor-fusion systems that transform synchronized sensor history, ego-motion, navigation context, and driving context into a unified spatiotemporal world representation. Build architectures that jointly reason over camera, LiDAR, radar, and vehicle-state inputs, with appropriate handling of calibration, synchronization, coordinate transforms, sensor latency, and uncertainty. Develop end-to-end and multi-task models that produce driving-relevant outputs from a shared scene representation, including; road graph elements such as lanes, boundaries, crosswalks, and traffic controls; semantic scene understanding; occupancy and free-space representations, including uncertain and occluded regions; Develop scalable multimodal fusion architectures, including Transformer-based early, late, and hierarchical fusion; BEV, point/voxel, and image-based representations; temporal context aggregation; and cross-modal attention. Create training, fine-tuning, and evaluation pipelines for large-scale multimodal datasets. Define multi-task objectives and metrics that balance perception quality, geometric consistency, prediction accuracy, latency, and safety-critical behavior. Investigate foundation-model approaches for autonomous driving, including vision-language models, multimodal pre-training, representation learning, and efficient deployment of learned world models. Work closely with perception, mapping, prediction, planning, simulation, data, and embedded-software teams to convert research advances into robust, production-quality AV systems. Develop systematic analysis and debugging tools for model failures, cross-sensor disagreement, long-tail scenarios, distribution shift, and regressions in closed-loop simulation and on-road evaluation. What We Need To See: BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field, or equivalent experience. 8+ years of experience, with at least 2+ years in the AV or robotics industry and 2+ years of leadership experience in a technically area Strong experience developing production-quality sensor-fusion, perception, state-estimation, or autonomous-driving systems. Demonstrated experience with learning-based multimodal perception or fusion involving two or more cameras, LiDAR, radar, map, navigation, and ego-motion signals. Solid understanding of 3D geometry, coordinate frames, calibration, temporal synchronization, ego-motion compensation, tracking, uncertainty estimation, and sensor failure modes. Experience with deep-learning methods for 3D perception, lego-context scene representation, occupancy/occlusion prediction, semantic segmentation, object detection/tracking, motion prediction, or planning. Strong C++ and Python programming skills, with hands-on experience developing, training, and optimizing deep-learning models in PyTorch. Experience with CUDA, distributed training, mixed-precision techniques, and efficient GPU inference using NVIDIA software and hardware is highly valued. Experience with Transformer, VLM, or multimodal foundation-model architectures, including pre-training, fine-tuning, distillation, quantization, or efficient inference. Experience training and evaluating models at scale, including distributed training, dataset curation, offline evaluation, simulation-based validation, and production monitoring. Ability to work across research and engineering boundaries: turn an ambiguous AV problem into measurable technical objectives, build the solution, and drive it to deployment. Ways To Stand Out From The Crowd: Experience building multi-task driving models that jointly predict perception, road structure, occupancy, motion, and/or trajectories from shared multimodal features. Experience with Transformer, VLM, or multimodal foundation-model architectures, including pre-training, fine-tuning, distillation, quantization, or efficient inference. Experience with BEV, point-cloud/voxel, neural scene representation, 3D reconstruction, occupancy-flow, or spatiotemporal world-model methods. Publications or open-source contributions in computer vision, robotics, machine learning, 3D perception, multimodal learning, or autonomous driving. Experience optimizing models for automotive-grade real-time deployment using NVIDIA GPUs, TensorRT, CUDA, or edge inference toolchains. We believe that building self-driving vehicles will be a defining contribution of our generation (e.g. traffic accidents are responsible for ~1.25 million deaths per year world-wide). We have the funding and scale, but we need your help on our team. NVIDIA is widely considered to be one of the technology world’s most desirable employers with some of the most forward-thinking people in the world working here. If you're entrepreneurial and autonomous, we want to hear from you! #AutonomousVehicles #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 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 22, 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.
AI Research Intern, TAO Multi-Modal Model Developm...
Our team is seeking to extend the internship of our current AI Research Intern for the TAO (Train, Adapt, Optimize) Multi-Modal Model Development project, recognizing their exceptional performance and strong alignment with the team’s research goals. Their innovative ideas and technical contributions have significantly enhanced our work. Given the rapidly evolving field of multi-modal AI, encompassing vision-language modeling, universal segmentation, and large-scale model training, extending this internship will provide further growth opportunities for the intern while strengthening our team’s capacity to develop scalable, high-impact AI solutions. Embark on an exciting journey with NVIDIA, a global leader in AI and accelerated computing. As an AI Research Intern focusing on multi-modal AI and vision-language model development within the TAO framework in Hanoi/HCM City, Vietnam, you will be at the forefront of advancing cutting-edge machine learning research. You’ll collaborate with a talented team of engineers and researchers dedicated to developing state-of-the-art deep learning models for tasks such as image segmentation, cross-modal understanding, and universal representation learning. This internship offers a unique opportunity to contribute to next-generation AI systems with real-world impact across industries—from autonomous vehicles to intelligent content understanding. What you'll be doing: Develop and fine-tune multi-modal AI models using NVIDIA’s TAO Toolkit and deep learning frameworks. Contribute to the design and implementation of vision-language models (VLMs) and universal segmentation systems. Conduct experiments and benchmarking to evaluate model accuracy, robustness, and scalability. Collaborate with cross-functional teams to integrate your research into production-level pipelines and NVIDIA SDKs. Participate in research discussions, code reviews, and technical documentation to share insights and improve methodologies. What we need to see: Currently pursuing a degree in Computer Science, Computer Engineering, or a related field. Proven experience with machine learning, deep learning, or computer vision model development. Strong Python programming skills and proficiency with PyTorch or similar frameworks. Solid understanding of neural network architectures, transformers, and multi-modal learning techniques. Excellent problem-solving abilities, attention to detail, and a collaborative mindset. Familiarity with vision-language models, image segmentation, or large-scale pretraining is a strong plus. Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/ 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.
Senior/Staff Machine Learning Research Scientist:...
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 In this role, you will collaborate closely with researchers and engineers on the Learned Behavior teams to tackle plan generation challenges in autonomous driving. You’ll apply state-of-the-art generative modeling techniques—ranging from cutting-edge diffusion, flow matching, energy-based models, and SoTA algorithms—in order to develop novel solutions that generate safe, comfortable, and efficient driving behaviors in the most challenging real world situations. Beyond core research, you’ll own the end-to-end lifecycle of your models, productizing them for robust, real-world autonomous driving deployments on a global scale. About the Work - Develop and scale state-of-the-art generative models—especially diffusion architectures, flow-matching techniques, and energy-based models —for autonomous plan generation. - Build generative models with foundation models. Leverage large language models and world foundation models for reasoning, decision making and multi-modality generation. - Optimize generative models using reinforcement learning to improve interactive reasoning. Explore reward modeling/learned verifier using generative models. Explore joint prediction and planning and self-play. Leverage generative models for active learning and world modeling. - Develop controllable generative models to guide the generation process towards desired goals, conditions and rewards. - Collaborate across autonomy teams while developing holistic solutions to top autonomy challenges. Understand issues, propose ideas, prioritize work and develop solutions to solve them, evaluate your solution by deploying the models on to the NuroDriver. About You You have deep expertise and prior experience in some or many of the following areas: - You have a Ph.D. (preferable) or M.Sc. with 3+ years of experience working with generative models in the lab, in industry, or both. - Research experiences in generative models, particularly diffusion models, flow matching and energy-based models, for robotics, including manipulation, path planning, and autonomous driving. Experiences in vision-language-action models, reinforcement learning for generative model optimization, video generation, text-to-image generation, diffusion models for LLMs, world foundation models, and other applications of generative modeling are a bonus! - You have strong problem solving and programming skills in Python and/or C++ - Strong culture fit and good team player. Demonstrated research publications in top conferences (e.g. NeurIPS, ICLR, ICML, CVPR, RSS, CoRL etc.) At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected pay range is between $193,930 and $352,290/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. 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.
Senior/Staff Engineer, Machine Learning - Online M...
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 Team Our robotics team is growing and we are looking for an ML Software Engineer to join our Online Mapping team. We are searching for an engineer with robotics and machine learning expertise to work on challenging problems in the design and implementation of onboard mapping models and algorithms, data and label management, as well as training pipelines. About the Role Building robust ML and/or mapping systems that work with real data (cameras, LiDAR, etc.) in uncertain environments, and a strong desire to contribute to the future of robot navigation for logistics and transportation. About the Work - Research, develop, and implement state-of-the-art online mapping models and algorithms. - Analyze and characterize the performance of the online mapping system, identifying opportunities for architecture, data or evaluation improvements in a E2E ML system. - Work cross functionally with other ML teams to integrate our models into centralized architectures. - Collaborate with stakeholders across autonomy, infrastructure, and systems teams on online mapping needs and requirements. About You - Proven record of solving in-production ML problems and making tradeoffs between data, model and evaluation. - Deep understanding of ML fundamentals with hands-on experience in training and evaluating modern ML models with applications in AV, robotics, mapping, computer vision, or related areas. - Prioritize impact and practicality, and make decisions to ensure on-time delivery of solutions. - Able to quickly iterate and experiment to cut through the noise and make well-informed, data-driven decisions. - Experience with robotics-related ML applications, 3D geometry. - Strong Python skills with experience in deep learning frameworks, e.g., PyTorch, TensorFlow, or Jax. Bonus Points - Proficiency in working with complex multi-component systems. - Experience in building ML pipelines and optimizing/productizing ML models. - Familiarity with modern ML tools and infrastructure such as distributed training and ML compilers. - Demonstrated research publications in top conferences (e.g. NeurIPS, ICLR, ICML, CVPR, RSS, CoRL, ICRA). - Deep understanding of 3D geometry and state estimation fundamentals. At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $$193,930 and $291,150 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package. At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #LI-DNP
Machine Learning Research Scientist: Generative Mo...
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 Work - Develop and scale state-of-the-art generative models—especially diffusion architectures, flow-matching techniques, and energy-based models —for autonomous plan generation. - Build generative models with foundation models. Leverage large language models and world foundation models for reasoning, decision making and multi-modality generation. - Optimize generative models using reinforcement learning to improve interactive reasoning. Explore reward modeling/learned verifier using generative models. Explore joint prediction and planning and self-play. Leverage generative models for active learning and world modeling. - Develop controllable generative models to guide the generation process towards desired goals, conditions and rewards. - Collaborate across autonomy teams while developing holistic solutions to top autonomy challenges. Understand issues, propose ideas, prioritize work and develop solutions to solve them, evaluate your solution by deploying the models on to the NuroDriver. About You You have deep expertise and prior experience in some or many of the following areas: - You have a Ph.D. (preferable) or M.Sc. with 2-3 years of experience working with generative models in the lab, in industry, or both. - Research experiences in generative models, particularly diffusion models, flow matching and energy-based models, for robotics, including manipulation, path planning, and autonomous driving. Experiences in vision-language-action models, reinforcement learning for generative model optimization, video generation, text-to-image generation, diffusion models for LLMs, world foundation models, and other applications of generative modeling are a bonus! - You have strong problem solving and programming skills in Python and/or C++ - Strong culture fit and good team player. - Demonstrated research publications in top conferences (e.g. NeurIPS, ICLR, ICML, CVPR, RSS, CoRL etc.) 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. 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.
Member of Technical Staff - Post Training, Applied
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 own applied post-training work end-to-end for text workloads, adapting Liquid Foundation Models for some of the world’s largest enterprise customers. You will act as the technical bridge between customer requirements and model delivery. You will lead engagements from scoping through evaluation, with full ownership over how text models are adapted and shipped. Between engagements, you will build reusable applied workflows and tooling that accelerate future delivery. If you care about data quality, evaluation design, and making language models actually work in production for real customers, this is the role. WHAT WE'RE LOOKING FOR We need someone who: - Takes ownership: Owns customer post-training projects end-to-end, from requirements through delivery and evaluation. - Thinks end-to-end: Can reason across data generation, instruction tuning, alignment, and evaluation as a single system. - Is pragmatic: Optimizes for model quality and customer outcomes over publications or theory. - Communicates clearly: Can translate between customer needs and internal technical teams, and push back when needed. THE WORK - Act as the technical owner for enterprise customer post-training engagements involving text workloads - Translate customer requirements into concrete post-training specifications and workflows - Design and execute data generation, filtering, and quality assessment processes for text corpora - Run supervised fine-tuning, instruction tuning, RLHF, DPO, and other preference alignment workflows - Design task-specific evaluations for text model performance and interpret results - Build reusable applied tooling and workflows that accelerate future customer engagements DESIRED EXPERIENCE Must-have: - Hands-on experience with data generation and evaluation for LLM post-training - Experience training or fine-tuning models using SFT, instruction tuning, RLHF, DPO, or similar preference alignment methods - Strong intuition for text data quality and evaluation design - Experience with text-specific post-training workflows: chat model alignment, instruction tuning, or text data curation at scale - Proficiency with open-source ML ecosystem (Hugging Face, PyTorch) and modern model architectures Nice-to-have: - Experience delivering applied ML work to external customers with measurable outcomes - Familiarity with inference optimization frameworks (vLLM, SGLang, TensorRT) - Experience building reusable ML tooling or evaluation infrastructure WHAT SUCCESS LOOKS LIKE (YEAR ONE) - Independently owns and delivers enterprise post-training projects for text workloads with minimal oversight - Is trusted by customers as the technical owner, demonstrating strong judgment and delivery quality - Has built reusable applied workflows or tooling that accelerate future customer engagements WHAT WE OFFER - Real ML work: You will fine-tune models, generate data, and ship solutions, not configure API calls. Your work feeds directly back into our core model development. - 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
Member of Technical Staff - ML Scientist, Japanese...
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 As an ML Scientist focused on post-training, you will improve the capabilities and behavior of Liquid Foundation Models for Japan and the global market. You will own the full experimental loop: identifying model weaknesses, developing data and training strategies, running controlled experiments, evaluating results, and turning successful ideas into high-quality checkpoints and reusable methods. You will work closely with Liquid's model, research, and infrastructure teams in Japan and at our US headquarters. Signals from real deployments will give you concrete, difficult problems to solve; your work will translate those signals into broader improvements in model quality, reliability, and usefulness. This is a role for a scientist who builds. Strong ideas matter, but so do careful data work, reliable implementations, and measurable improvements in working models. WHAT WE'RE LOOKING FOR We need someone who: - Thinks experimentally: You form clear hypotheses, establish strong baselines, design informative ablations, and let evidence guide the next step. - Understands models through data: You can identify gaps in training data and design curation, filtering, generation, and supervision strategies to address them. - Owns the full post-training loop: You are comfortable moving between datasets, training, evaluation, error analysis, and implementation. - Builds with rigor: Your experiments are reproducible, your conclusions are well supported, and successful research can be integrated into shared systems. - Collaborates across boundaries: You communicate clearly with globally distributed research, engineering, and applied teams. THE WORK - Design and execute post-training strategies for language and multimodal models, including supervised fine-tuning, preference optimization, reinforcement learning, and distillation. - Build and curate high-quality training data using human, synthetic, and model-generated signals, with particular attention to Japanese-language and domain-specific capabilities. - Develop evaluations that expose meaningful capability and reliability gaps across Japanese and global use cases. - Conduct systematic error analysis and use the results to improve data mixtures, objectives, training methods, and model behavior. - Run controlled experiments and ablations, interpret results, and communicate clear recommendations. - Develop reliable, scalable training and evaluation pipelines in collaboration with model infrastructure teams. - Contribute methods, tooling, datasets, and findings that accelerate post-training work across Liquid AI. DESIRED EXPERIENCE Must-have: - Hands-on experience post-training modern language or multimodal models. - Strong understanding of machine learning fundamentals and current post-training and RL methods. - Solid engineering skills and proficiency with the open-source ML ecosystem. - Experience designing and running rigorous experiments, including baselines, ablations, and systematic error analysis. - Experience building, curating, or assessing training and evaluation data at a meaningful scale. - Ability to turn research ideas into reliable implementations and measurable model improvements. - Proficiency in English, including the ability to collaborate on complex technical work with global teams. - Experience leveraging agents to amplify your own work. Nice-to-have: - Experience with preference optimization or reinforcement learning methods for foundation models. - Experience post-training multimodal models involving text, vision, or audio. - Experience developing synthetic data pipelines, reward models, verifiers, or model-based evaluations. - A track record of producing useful research artifacts, such as strong models, datasets, open-source systems, or technical reports. - Reading proficiency in Japanese. We care about demonstrated ability, scientific judgment, and the quality of your work. Degrees and publications are not required, and we encourage you to apply even if your experience does not match every item above. WHAT SUCCESS LOOKS LIKE (YEAR ONE) 1. You deliver checkpoints with clear, measurable improvements on important Japanese and global capabilities. 2. You establish evaluations and error-analysis practices that reliably identify model weaknesses and guide post-training priorities. 3. You develop data or training methods that become part of Liquid's repeatable post-training workflow. 4. You run high-quality experiments at increasing scale and turn the results into clear technical decisions. 5. Your work materially influences Liquid's model roadmap and improves systems used in real deployments. HOW WE WORK This is a primarily remote role for candidates already based in Japan. We typically meet in our Tokyo office once or twice a week. Working hours are fully flexible, with an expectation that you can collaborate effectively with customers in Japan and colleagues in other time zones. The role includes travel to the United States and to relevant conferences, as well as occasional travel within Japan. WHAT WE OFFER - High-impact model work: Improve the capabilities and behavior of foundation models used in real products and deployments. - Direct access to core teams: Collaborate closely with the researchers and engineers building Liquid's models and training systems. - Ownership in a growing market: Help define our post-training approach in Japan at a time of strong customer demand and rapid growth. - Supportive & growth-driven culture: Thrive in a collaborative, feedback-rich environment that prioritizes continuous learning, mentorship, and personal career development. - Flexibility: Primarily remote work, flexible working hours, and unlimited paid time off. - Competitive rewards: Competitive salary, equity in a unicorn-stage company, and standard benefits for employees in Japan.
Member of Technical Staff - ML Research Engineer,...
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 Our Data team powers Liquid Foundation Models across pre-training, vision, audio, and emerging modalities. Public data sources are plateauing. Model performance increasingly depends on purpose-built datasets. We need ML-minded engineers who can collect, filter, and synthesize high-quality data at scale. We treat data as a research problem, not an infrastructure problem. Our engineers run experiments, design ablations, and measure how data decisions move model quality. We will match you to the team where you can grow the fastest and have the most impact: pre-training, post-training RL, vision-language, audio, or multimodal. While San Francisco and Boston are preferred, we are open to other locations. WHAT WE'RE LOOKING FOR We need someone who: - Thinks like a researcher, ships like an engineer: We need people who form hypotheses, run experiments, and measure results. Our engineers understand deep-theoretical research, and our researchers ship production systems. - Learns fast and adapts: We work across modalities that evolve weekly. We need people who pick up new domains quickly and thrive with ambiguity. - Obsesses over data quality: We believe data quality is non-negotiable. Filtering, deduplication, augmentation, and evaluation are first-class concerns for our team, not afterthoughts. - Solves problems independently: Our data engineers sit within training groups (pre-training and multimodal). We collaborate closely, but we expect ownership and self-direction. THE WORK - Build and maintain data processing, filtering, and selection pipelines at scale - Create pipelines for pretraining, midtraining, SFT, and preference optimization datasets - Design synthetic data generation systems using LLMs, structured prompting, and domain-specific generators - Design and run evaluations and ablations to measure dataset's impact on model performance - Monitor public datasets across text, vision, and audio domains - Collaborate with pre-training, vision, and audio teams on modality-specific data needs DESIRED EXPERIENCE Must-have: - Strong Python skills with the ability to quickly comprehend problems and translate them into clean, working code - Solid ML fundamentals: experience training, evaluating, and iterating on models (PyTorch preferred) - Track record of learning new technical domains quickly - 3+ years relevant experience with an M.S., or 1+ year with a Ph.D. (5+ years with a B.S.) Nice-to-have: - Experience with synthetic data generation, data curation, or ML evaluation (designing evals, benchmarking, measuring data and model quality) - Experience with LLMs, VLMs, computer vision, or audio data pipelines - Open-source contributions or publications at NeurIPS, ICML, ICLR, or CVPR WHAT SUCCESS LOOKS LIKE (YEAR ONE) - You own a critical data pipeline end-to-end for one of our modalities - You have built or improved data systems that measurably moved model performance - You have identified and integrated at least one external dataset that moved the needle WHAT WE OFFER - Impact at scale: Your pipelines directly determine model quality across all of Liquid's foundation models. - 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
Liquid Labs - Research Engineer
ABOUT LIQUID LABS Research has been core to Liquid AI from the beginning. Liquid Labs gives that work a formal home; an internal research accelerator driving fundamental breakthroughs in the science of building intelligent, personalized, and adaptive machines. Our origins trace back to MIT CSAIL, where the foundational work on Liquid Neural Networks defined a new class of dynamical, efficient sequence-processing architectures. That research became the basis for Liquid Foundation Models (LFMs). Scalable, multimodal models built for real-world deployment in resource-constrained environments. At Liquid Labs, we extend that lineage - pushing forward the frontier of efficient, adaptive intelligence through both fundamental research and practical engineering. We work hand-in-hand with Liquid’s core foundation model and systems teams to translate theory into deployed capability — defining a new generation of intelligent systems that are both powerful and efficient. ABOUT THE ROLE: As a Research Engineer, you’ll join a small, high-context team exploring the limits of adaptive intelligence. You’ll design and implement novel architectures, training methods, and inference strategies to redefine what efficient AI can do. You’ll operate at the intersection of research and engineering — translating scientific ideas into working systems, publishing where it drives the field forward, and deploying where it changes what’s possible. While San Francisco and Boston are preferred, we are open to other locations in the United States. THIS ROLE IS FOR YOU IF: - Work fluently in Python and frameworks such as PyTorch, JAX, or TensorFlow - Have experience in machine learning research or production-grade ML systems - Move fast from paper to prototype — curiosity backed by precision - Care about efficiency, scalability, and elegant system design as scientific principles - Value small, deep-technical teams where impact is immediate and measurable - Have a track record of publication in tier-1 venues (NeurIPS, ICML, ICLR, CVPR, ACL, or equivalent), demonstrating original contribution and research rigor OPEN SCIENCE AND IMPACT Liquid Labs reinforces our commitment to transparent, reproducible, open research. We publish through technical reports, architectural deep dives, ablations, and model releases, advancing the broader science of efficient AI while translating breakthroughs into production-ready systems. WHY LIQUID LABS Liquid Labs is for researchers who build. Those who care about lasting impact more than publication count, but who hold themselves to the same scientific standard. We don’t chase benchmarks; we redefine them. We move fast, think deeply, and measure success by the systems that endure. There is no application deadline. We review candidates on a rolling basis.
ML Engineer - Personalization & Recommendation Sys...
ABOUT KREA At Krea, we are building next-generation AI creative tools. We're dedicated to making AI intuitive and controllable for creatives - our mission is to build tools that empower human creativity, not replace it. We believe AI is a new medium that allows us to express ourselves through various formats - text, images, video, sound, and even 3D. We're building better, smarter, and more controllable tools to harness this medium. We recently took this a step forward with the launch of Krea 2 https://www.krea.ai/blog/krea-2-image-model, our first foundation model, built completely from scratch for aesthetic diversity and stylistic control. We've raised over $83M and are backed by world-class investors such as a16z, Bain Capital, and Abstract. We work full-time and in-person at our waterfront office in San Francisco. We care about creativity: our team includes musicians, designers, visual artists, and engineers. THIS ROLE We’re looking for an ML Engineer to architect and build Krea’s personalization and recommendation systems from scratch. You’ll have full ownership over how we understand user taste, curate content, and adapt generative models to individual aesthetics. This is a role at the intersection of recommendation systems, representation learning, and generative image and video tools. WHAT YOU’LL DO - Architect and build Krea’s personalization and recommendation stack from the ground up, owning the technical direction end to end - Design algorithms to model user preference and taste, enabling Krea’s models to adapt to individual styles and aesthetics - Build high-quality, curated feeds that balance exploration, personalization, and aesthetic coherence - Work directly with the our model and research team to co-design personalization mechanisms that influence how our generative models learn, adapt, and express style - Contribute to personalized image generation research, with a focus on style, taste and subjective quality - Collaborate closely with product, design, and research to define what “good personalization” means in a creative context - Take systems from research and prototyping through production, iteration, and continuous improvement WHAT WE’RE LOOKING FOR - Strong experience building recommendation systems or personalized feeds from scratch - Proven ability to design and ship high-quality curated content experiences - Experience working with media-based personalization (image, video preferred; music or other modalities also welcome) - Solid foundations in machine learning, representation learning, and modern deep learning techniques - Strong Python skills and experience with ML frameworks such as PyTorch or JAX - Ability to operate independently, make architectural decisions, and own complex systems end to end NICE TO HAVE - Experience with large-scale data systems and production ML infrastructure - Prior work on or familiarity with diffusion models or generative image systems - Background in embeddings, similarity search, ranking, or aesthetic evaluation - Interest in creative tools, art, design, or generative media WHAT WE OFFER - Team: Work alongside a world-class team building the future of AI creative tooling - Impact: Significant scope and company-wide impact - Competitive compensation: generous salary & equity packages - Health & wellness: 100% health & 99% dental/vision insurance premiums covered for employees, health FSA accounts, & long-term disability coverage - Time off: Flexible PTO policy - Financial planning: 401k with a 4% company-sponsored match - Meals in the office: breakfast, lunch, dinner - you name it, we'll cover it - Transit: Ubers covered to & from the office - Sponsorship: We're open to sponsoring international visas where we can (e.g., STEM OPT, OPT, H-1B, O-1, E-3). - And more! Please note the above benefits & perks are for full-time employees
AI Researcher
Hume AI is seeking talented AI researchers interested in working with our team to build state-of-the-art speech-language models (SLMs). Our new SLM training method—reinforcement learning from human expression (RLHE)—learns human preferences from behavior in millions of audio and video recordings, making SLMs superhumanly helpful, interesting, funny, eloquent, honest, and altruistic. Join us in the heart of New York City and contribute to our endeavor to ensure that AI is guided by human values, the most pivotal challenge (and opportunity) of the 21st century. About Us Hume AI is a Series B startup dedicated to building artificial intelligence that is directly optimized for human well-being. As the first company to release speech language models, we’re focused on expanding our research to encompass audio understanding models and evaluation platforms for enterprises. Our goal is to enable a future in which technology draws on an understanding of human emotional expression to better serve human goals. As part of our mission, we also conduct groundbreaking scientific research, publish in leading scientific journals like Nature , and support a non-profit, The Hume Initiative, that has released the first concrete ethical guidelines for empathic AI ( www.thehumeinitiative.org ). You can learn more about us on our website ( https://hume.ai/ ) and read about us in WIRED , Forbes , and Venturebeat . About the Role We are looking for talented researchers with relevant publications, 2+ years of experience in a research role in industry, and/or a proven track record of contributions to open source language, audio, or video models. (A PhD is neither necessary nor sufficient.) We're looking for someone excited to work with our team to build state-of-the-art audio models and evaluation tools. In this role, you will build out software systems that support distributed model training, inference, and benchmarking as well as massive-scale data collection, storage, preprocessing, and analysis. You will be working to solve some of today’s most exciting AI research problems at industry scale. Requirements - Expertise in the Python ecosystem and popular ML libraries and tools (e.g. PyTorch) - Experience writing robust and maintainable production-ready code - Comfort iterating quickly on new and uncertain research directions - 2+ years of experience contributing to the training and/or fine-tuning of models with large-scale datasets of text, audio, image, and/or video data (predictive or generative) Application Note Please apply only to the position that best aligns with your qualifications. If you submit multiple applications or have applied within the past 6 months, only your initial submission will be considered. Annual Salary $180,000 - $350,000 USD
Senior Machine Learning Engineer, Voice Agents - E...
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 3M+ models, 1M+ datasets & 1.47M+ apps. Our open-source libraries have more than 600k+ stars on Github. About the Role We are building the open voice-agent stack for Hugging Face, and we are looking for a senior engineer to own a large part of it.Two things sit at the centre of this role. The first is speech-to-speech , our open-source library for realtime voice agents. The second is hf-voice, a new product that will let any developer build and deploy voice agents with their Hugging Face account. The library already powers the Reachy Mini fleet and there is a public demo running on Spaces, so you won't start from a blank page. But almost everything about how this becomes a product developers rely on is still open, and you will have a direct say in it. Your missions: - Own the Open-Source Library: Take architectural ownership of large parts of speech-to-speech: pipeline design, latency budget, and the reliability of the realtime loop. Integrate new ASR, TTS and end-to-end speech models as they land, and keep the abstractions clean while the model landscape keeps moving. Review community PRs, triage issues, cut releases, and grow the group of contributors around the project. - Ship hf-voice: Design the developer API and the streaming protocol: session lifecycle, transport (WebSockets/WebRTC), authentication, error semantics, versioning. Build the serving side: realtime inference on GPU, concurrency, autoscaling, observability, and cost per session. Work with the Hub and inference teams so that a working voice agent is easy to integrate into products and demos. Take the product from demo to production: load testing, SLOs, graceful degradation when a model or a network path misbehaves. - Work in the Open: Write the docs, examples and templates that get a developer from zero to a running agent in minutes. Support the deployments already relying on the stack, starting with the Reachy Mini fleet. Talk about the work publicly if you enjoy it: blog posts, demos, conference talks. We cover the travel and the prep time. Requirements What we're looking for Senior engineer, able to own a substantial part of an architecture and drive it forward autonomously. Experience building developer-facing infrastructure at an AI or developer-tools company: inference APIs, agent infrastructure, or something comparable. Substantial open-source contributions to a Python library. Comfortable with async Python and distributed systems, including their failure modes. You have shipped something realtime: streaming, WebSockets or WebRTC, audio or video pipelines, live inference. Practical experience with LLMs or multimodal models in production. Clear written communication and a habit of collaborating async and in public. Motivated by voice and conversational AI. Bonus points if you have Contributions to a voice-agent framework such as speech-to-speech, pipecat, LiveKit Agents, Vocode or TEN. Contributions to llama.cpp or another low-level inference runtime. Hands-on work with ASR, TTS or end-to-end speech models, including evaluation of latency and quality trade-offs. GPU serving, quantization, or on-device inference experience. Audio pipeline knowledge: VAD, echo cancellation, jitter buffers, barge-in and turn detection. Experience shipping to embedded or robotics targets. A public track record: talks, blog posts, demos. About You 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.
Machine Learning Engineer, Ads
WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $700M 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 exceptional Machine Learning Engineers focused on Ads to help take Higgsfield's advertising platform to the next level. You'll work at the intersection of large-scale machine learning, generative AI, and advertising systems—building the models and infrastructure that determine how creative is generated, ranked, optimized, and ultimately performs. This role is for someone who understands ads systems deeply and is equally strong in modern generative AI. You should be comfortable moving across ranking and recommendation, targeting and optimization, prompt engineering, post-training, and production ML systems. You'll help define what an AI-native advertising platform looks like from the ground up. WHAT YOU'LL DO: - Build and improve ML systems powering advertising products, including ranking, recommendation, targeting, prediction, and optimization. - Develop models that improve ad creative quality, relevance, personalization, and performance at scale. - Build systems that connect generative models with real-world advertising performance signals, creating feedback loops that continuously improve model outputs. - Apply prompt engineering and post-training techniques to improve generative models for advertising and creative use cases. - Work on fine-tuning, preference optimization, evaluation, and other techniques for adapting foundation models to specific creative and advertising objectives. - Design and run experiments across creative generation, ranking, targeting, and delivery to understand what drives advertiser performance. - Build production ML systems that operate reliably at significant scale, from experimentation through inference and serving. - Work closely with Product, Research, Engineering, and GTM teams to turn advances in generative AI into products advertisers can use. REQUIREMENTS - Deep experience building machine learning systems for advertising. - Strong understanding of ads systems, including areas such as ranking, recommendation, targeting, bidding, conversion prediction, creative optimization, or measurement. - Hands-on experience with LLMs, multimodal models, or generative AI systems. - Strong experience with prompt engineering and model evaluation. - Experience with post-training, including techniques such as supervised fine-tuning, preference optimization, reinforcement learning, or related approaches. - Strong software engineering fundamentals and experience shipping production ML systems. - Ability to operate across research and engineering: you can experiment quickly, identify what works, and turn it into a scalable production system. - High agency. - Working English. NICE TO HAVE - Experience building ads, ranking, or recommendation systems at a major consumer, social, search, or advertising platform. - Experience with generative video, image, or multimodal models. - Experience using downstream signals such as CTR, CVR, ROAS, engagement, or retention to train or optimize ML systems. - Experience with large-scale model training, inference optimization, or distributed ML infrastructure. - Experience building AI systems that generate or optimize advertising creative. COMPENSATION & BENEFITS • We offer a competitive and thoughtfully structured compensation package designed to align with impact, experience, and long-term growth. • Base Salary: The anticipated salary range for this role is $165 - $230k, depending on experience, skills, scope, and location. • Equity: In addition to cash compensation, employees are eligible to participate in the company’s stock option program and share in Higgsfield’s long-term growth. • • Benefits: We offer a comprehensive benefits package designed to support employees professionally and personally • The opportunity to work on ambitious AI products alongside a highly experienced, fast-moving, and international team. • Significant ownership, direct impact, and opportunities for professional growth as the company scales. This is a hybrid role based in the San Francisco Bay Area. Team members are expected to work from our San Francisco office three full days per week, with the remaining days worked remotely and are expected to be available during agreed working hours and to maintain sufficient overlap with the relevant team’s time zone. We value in-person collaboration, active communication, responsiveness, and close partnership across teams. Higgsfield AI is an equal opportunity employer. We are committed to building a diverse and inclusive team and do not discriminate on the basis of race, color, religion, sex, gender identity or expression, sexual
Research Engineer
OVERVIEW: Hedra is a pioneering generative modeling company — first models to market — now building a Physical AI team to bring these models to real-world industry and economy use cases. As a Research Engineer on our Physical AI team, you will lead pre-training and post-training on action-conditioned world models, working hand-in-hand with industrial partners to close the loop between generative AI and physical systems. This is not a black-box applied role: your work will be published, your infrastructure will be serious, and your impact will be direct. If you want to work at the frontier of generative modeling and physical AI, this is the team. RESPONSIBILITIES: - Design, implement, and run pre-training and post-training pipelines for action-conditioned world models and vision-language-action (VLA) models - Develop and refine training methodologies, including fine-tuning, reinforcement learning, and large-scale multimodal learning - Design and generate training and evaluation datasets from simulation, including environment setup, domain randomization, and sim-to-real transfer strategies - Build distributed training infrastructure using PyTorch, FSDP, and DeepSpeed - Work with multimodal data pipelines involving video, sensory inputs, and action sequences - Evaluate model performance using both benchmark datasets and real-world deployment metrics - Contributions research publications a plus - Collaborate with industrial partners to adapt generative models for real-world physical AI applications QUALIFICATIONS: - Experience with pre-training or post-training on large generative models (video, multimodal, or action-conditioned) - Hands-on proficiency with PyTorch and distributed training frameworks (FSDP, DeepSpeed) - Strong fundamentals in machine learning, optimization, and large-scale data processing - Familiarity with VLMs, VLAs, or world models - Background in robotics, embodied AI, or sim-to-real transfer is a plus - Experience with video understanding or temporal reasoning is a plus - BS/MS/PhD in Computer Science, Machine Learning, Robotics, or a related field BENEFITS: - Competitive compensation and equity - 401k (no match) - Healthcare (Silver PPO Medical, Vision, Dental) - Lunch and snacks at the office We encourage you to apply even if you don't fully meet all the listed requirements; we value potential and diverse perspectives, and your unique skills could be a great asset to our team.
Research Engineer, Post-Training
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 Post-training is how Harvey turns expert feedback and agent traces into models that are meaningfully better at legal work. We are looking for a research engineer who can help scale that loop: defining and running model training experiments, interpreting results, and working with internal and external research partners to build better data, environments, graders, and training recipes. This role is for someone who can self-manage model training and applied research projects. You will work closely with internal and external research collaborators on post-training efforts that matter to our product roadmap. The ideal candidate has extensive hands-on experience training open weight models, either in a research or production setting, and enough engineering depth to run and debug experiments efficiently. WHAT YOU'LL DO - Drive post-training experiments, pushing agent performance while navigating the Pareto frontier of cost, latency, security, and governance. - Optimize agent harnesses, including domain-specific skills, tools, subagents, retrieval strategies, and validation loops that improve quality on long-horizon legal work. - Design and develop grading and reward systems that are reliable enough for evaluation, efficient enough for iteration, and strict enough for high-stakes legal work. - Study agent behavior, identifying patterns that correlate with successful work product, and converting those findings into training data, evals, or harness changes. - Work with Harvey researchers and external research partners to define experiments, evaluate methodology, review results, and keep projects moving toward concrete model improvements. WHAT YOU HAVE - Hands-on experience with post-training or model-training work, such as SFT, preference optimization, RLHF/RLAIF, reward modeling, distillation, or adapting open-weight models to specialized domains. - Strong judgment about model behavior: you can read traces, inspect outputs, identify failure modes, and reason about whether a metric is measuring the thing that matters. - Strong Python and research-engineering ability. You can write clean code, debug experiments, and build the simple but reliable systems needed to make research move faster. - Ability to self-manage ambiguous applied research projects and communicate clearly with researchers, engineers, product teams, domain experts, and external partners. Nice to Have - Experience building data or evaluation infrastructure for ML workflows, such as dataset curation pipelines, model-output processing, experiment tracking, evaluation dashboards, or regression analysis tooling. - Experience with distributed training, inference systems, GPU workloads, or large-scale ML experimentation. - Research publications, open-source contributions, or shipped industry work in LLMs, agents, evaluation, or ML systems. COMPENSATION $231,000 - $340,000 DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES [HERE https://www.notion.so/harveyai/Harvey-Candidate-Privacy-Policies-319ac3fcdd7a803bb807d5094f249922]. #LI-AK1 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
AI Model Policy Trainer, Image Evaluation - Seattl...
ABOUT HANDSHAKE Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions. Handshake AI works directly with frontier AI lab researchers to create evaluations, publish benchmarks, and improve AI models through human expertise. ROLE DETAILS Location: Onsite in Seattle, WA, Monday-Friday. Compensation: $36-$72/hr. Placement within the range depends on experience. Employer: TCWGlobal. This is a W-2 assignment supporting Handshake AI. Employment: Full time, 40 hours per week, non-exempt and eligible for overtime pay. Schedule: Monday-Friday, 8 a.m.-5 p.m. PT. ABOUT THE ROLE As an AI Image Evaluator, you will help image generation models learn two things at once: what a good image is, and what an acceptable image is. You will look at prompts and the images a model produced from them, then answer questions like: Did the image do what the prompt asked? Is it well made, or does it have the hands, lighting, text, and anatomy problems that give generated images away? Which of two images is better, and why? Does the image violate the customer's content policy, and if so, which category and how severely? Does it depict a real person, a protected brand, or a minor in a way the policy does not allow? The interesting cases are the close ones. Two images that look nearly identical until you notice one has a logo in the background. A stylized nude that is fine as figure study and not fine with one change of pose. A prompt that asked for "a realistic photo of a senator" and a model that complied. A beautiful image that ignored half the prompt, next to an ugly one that nailed it. We are looking for people who already see images critically, whether that came from photography, illustration, design, years inside Midjourney and Stable Diffusion, or moderating visual content at scale. You do not need all of these. You need one deep, and the judgment to learn the rest. This is not rote annotation. Rubrics cannot anticipate every image, and good evaluators do not apply them mechanically. You will balance the rubric's text and intent with customer expectations, precedent, and team calibration, and you will explain your reasoning clearly enough that it can train a model. WHAT YOU WILL DO - Evaluate generated images against their prompts for adherence, composition, realism, style consistency, and technical defects; maintain accuracy and consistency across repeated evaluations - Compare images side by side and select the stronger one with a clear, evidence-based rationale - Classify images against customer content policies covering sexual content, violence, hate symbols, real-person likeness, intellectual property, and depictions of minors - Select the most defensible classification when an image is genuinely ambiguous, and write concise rationales that cite rubric language and specific visual details - Distinguish "I do not like this" from "this fails the prompt" from "this violates policy," and keep those judgments separate while applying customer policy consistently - Write and refine prompts that probe where a model's quality or safety behavior breaks down - Identify rubric gaps, contradictions, and emerging edge cases, and raise them with project leads and policy teams - Participate actively in calibration discussions; challenge interpretations respectfully and update your judgment when stronger reasoning emerges YOU MAY BE A FIT IF - You have a trained eye from photography, illustration, concept art, art direction, retouching, photo editing, VFX, or visual design, and you can say precisely why one image is better than another - You use generative image tools heavily (Midjourney, Stable Diffusion, ComfyUI, Flux, DALL-E, Ideogram) and know their failure modes, their prompt quirks, and how their safety filters get bypassed - You have moderated or reviewed visual content at scale and have applied a policy taxonomy to borderline images under time pressure - You notice small details: an extra finger, a mismatched shadow, a brand mark, a face that is a little too familiar - You can hold a rubric steady across a long session of near-identical images and treat sensitive material with maturity and sound judgment - You can hold a strong opinion without becoming attached to being right - You explain judgment calls clearly and precisely in writing so another person can audit your reasoning - You can separate your personal taste from the standard a customer has asked you to apply Strong candidates may come from photography, illustration, graphic or UX design, art direction, photo editing, animation or VFX, game art, trust and safety, content moderation, brand or IP enforcement, ad review, or art education. We care more about how you see and how you reason than where you learned to do it. A degree and a technical background are not required. NICE TO HAVE - A public portfolio, publication credits, or a body of generative work (Civitai, Discord communities, LoRA or model training, published prompt work) - Experience judging images comparatively: portfolio review, photo competition judging, creative A/B testing, art school critique - Formal training in anatomy, color, lighting, or composition - Content moderation or trust and safety experience on an image-heavy platform - Working knowledge of copyright, trademark, and right-of-publicity basics - Prior work in AI evaluation, RLHF, image labeling, or data annotation - Familiarity with calibration sessions, inter-rater agreement, or adjudication workflows SENSITIVE-CONTENT NOTICE This role involves regular and deliberate engagement with sensitive imagery. Depending on the project, evaluations may include sexual content and nudity, graphic violence and gore, hate symbols, self-harm, and depictions of real people and of minors in contexts that must be assessed against policy. Some of this material is disturbing by design, because the purpose of the work is to teach models not to produce it. The work is conducted within structured evaluation frameworks and professional guidelines, with exposure limits, content rotation, mandatory reporting protocols for illegal material, and access to mental health support. Candidates must be able to engage with this material carefully, responsibly, and sustainably while maintaining sound judgment and consistent work quality. BENEFITS Benefits are provided through TCWGlobal. Eligible employees can enroll in medical coverage, including prescription and mental health benefits, dental and vision insurance, healthcare and dependent care flexible spending accounts, and pretax commuter benefits. A 401(k) retirement plan with an employer match is available to eligible participants. Additional offerings include voluntary accident, critical illness, and term life insurance, wellness and pet-related reimbursements, charitable matching, and employee discounts. Eligibility requirements, waiting periods, employee contributions, and plan terms apply. Full-time employees scheduled for at least 30 hours per week are eligible for health coverage beginning the first of the month following at least 30 days of employment. Retirement plan eligibility follows a separate schedule. Paid time off: PTO accrues from the first day of the assignment at one hour per 40 hours worked, with no waiting period to use accrued PTO. PTO may be used for vacation, personal days, or illness, subject to policy approval requirements. Accrual is capped at 80 hours per year and balances at 100 hours, unless otherwise required by law. Additional paid sick leave is provided in accordance with applicable Washington and Seattle requirements. Paid holidays: The holiday policy lists 13 paid holidays. Eligible employees receive eight hours of holiday pay at their base hourly rate for observed holidays that fall on a scheduled workday, subject to the holiday policy. See the TCWGlobal 2026 Benefits Guide https://www.tcwglobal.com/benefitsguide-basic-2026 for coverage options, costs, and enrollment details. INTERVIEW PROCESS The process includes application review, a recruiter screen, a skills assessment, a hiring manager screen, onsite interviews, and a final round before the offer stage. Your recruiter will share the next steps as you move through the process. EQUAL OPPORTUNITY AND ACCOMMODATIONS TCWGlobal is an equal opportunity employer. Hiring decisions are based on qualifications and abilities, without discrimination based on characteristics protected by applicable law. Reasonable accommodations are available during the application and interview process. If you need an accommodation, please let your recruiter know.
Senior Machine Learning Engineer (Large Systems)
About Graphcore At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence. Job Summary As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field. The Team The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications. If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you! Responsibilities and Duties - Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators. - Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews. - Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency. - Design and conduct experiments on novel AI methods, implement them and evaluate results. - Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware. - Engage with AI community and keep in touch with the latest developments in AI. Candidate Profile Essential: - Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field. - Proficiency in deep learning frameworks like PyTorch/JAX. - Strong Python or C++ software development skills - Expertise in deep learning from model training to optimisation and evaluation. - Capable of designing, executing and reporting from ML experiments. - Developed deep understanding of performance bottlenecks and how to overcome them. - Ability to move quickly in a dynamic environment - Enjoy cross-functional work collaborating with other teams. - Strong communicator - able to explain complex technical concepts to different audiences. Desirable: - Experience in one or more of: - MLOps for Kubernetes-based clusters - Building production systems with large language models - Efficient computing based on low-precision arithmetic. - Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models. - Experience in distributed training or inference of ML models across 64+ accelerators. - Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies. - Have contributed to open-source projects or published research papers in relevant fields. - Knowledge of cloud computing platforms. - Keen to present, publish and deliver talks in the AI community. Benefits In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments. Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications
Research Scientist (diffusion)
We are Genmo, a research lab developing the world’s most sophisticated video world models to understand, simulate, and interact with the physical world. Our mission is to unlock the right brain of AGI. Join us in advancing physical intelligence and enabling robots to learn and act in a changing world. ROLE OVERVIEW: We are seeking an exceptional Research Scientist to join our team, focusing on developing cutting-edge diffusion models for text-to-video generation. In this role, you will be at the forefront of innovation, creating novel architectures and algorithms that transform written descriptions into stunning, coherent video content. KEY RESPONSIBILITIES: - Lead research initiatives in advanced diffusion models for text-to-video generation, focusing on improving visual quality, temporal consistency, and semantic fidelity - Develop and implement state-of-the-art algorithms for translating textual descriptions into dynamic video content - Design and conduct rigorous experiments to validate new ideas and evaluate model performance - Collaborate with cross-functional teams to integrate research breakthroughs into our production pipeline - Stay at the cutting edge of the field by regularly reviewing academic literature and attending top-tier conferences - Contribute to the research community through high-quality publications and open-source contributions - Mentor junior researchers and foster a culture of innovation within the research team - Work closely with product teams to align research directions with user needs and market opportunities QUALIFICATIONS: - Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field - Must have: - Strong publication record in top-tier conferences (e.g., CVPR, ICCV, NeurIPS, ICML) with a focus on generative models, particularly diffusion models - Extensive experience implementing and optimizing large-scale generative models for image or video tasks - Deep understanding of state-of-the-art techniques in text-to-image and text-to-video generation - Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow - Excellent communication skills with the ability to explain complex technical concepts to diverse audiences - Proven ability to work collaboratively in a team environment - Ideal candidate will have: - Postdoctoral or industrial research experience in generative AI for video - Hands-on experience with text-to-video generation projects - Expertise in other generative model architectures (e.g., GANs, VAEs) and their applications to video - Experience working with large-scale datasets and distributed computing environments - Track record of successful collaboration with product teams on technology transfers - Familiarity with video codecs, compression techniques, and perceptual quality metrics - Contributions to open-source projects in the field of generative AI ADDITIONAL INFORMATION The role is based in the Bay Area (San Francisco). Candidates are expected to be located near the Bay Area or open to relocation. Genmo is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law. Genmo, Inc. is an E-Verify company and you may review the Notice of E-Verify Participation https://drive.google.com/file/d/1urlF3XKQTw83aJfpMKMwrUP1E1mUlNTO/view?usp=sharing and the Right to Work posters in English and Spanish https://drive.google.com/file/d/1ZD4zax6Li8oTN_VHd9w3rpFPoWeaeGVO/view?usp=drive_link.
Research Scientist: Pretraining
ABOUT GENERALIST At Generalist, we are on a mission to build general intelligence for the physical world and make it useful to everyone. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done. We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world. The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google DeepMind, and other frontier labs—with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models and vision-language-action models (PaLM-E, https://research.google/blog/palm-e-an-embodied-multimodal-language-model/ RT-2 https://deepmind.google/blog/rt-2-new-model-translates-vision-and-language-into-action/, Gemini Robotics https://deepmind.google/models/gemini-robotics/), launched and scaled ChatGPT https://chatgpt.com/ and GPT-4 https://openai.com/index/gpt-4-research/ to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas https://bostondynamics.com/atlas/, Spot https://bostondynamics.com/products/spot/, Stretch https://bostondynamics.com/products/stretch/) and pushed the limits of what they can do (from parkour https://www.youtube.com/watch?v=tF4DML7FIWk to manipulation https://bostondynamics.com/blog/large-behavior-models-atlas-find-new-footing/, and testing robustness https://www.youtube.com/watch?v=aFuA50H9uek). We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. ABOUT THE ROLE You will build the base intelligence layer for robotics. We train large-scale robot foundation models from massive multimodal datasets spanning video, proprioception, action traces, language, and more. You will design and run the core large-scale training efforts that give our models fundamentally new general capabilities across embodiments, tasks, and environments. You will “live and breathe” all forms of robot data. If you have worked on pretraining large-scale ML systems or generative foundation models e.g. multimodal, language, audio, etc. (not only robotics), then this may be the role for you. You’ll be responsible for: - Designing and executing large-scale pretraining runs for robot foundation models (transformer- and diffusion-based architectures) - Defining model architectures, objectives, and training curricula across multimodal robotic data (vision, action, state, language) - Developing scalable data mixtures and sampling strategies across petabyte-scale datasets - Guiding data collection operations towards new directions, as well as sourcing new datasets - Running ablations to understand scaling laws, data quality effects, and architecture tradeoffs - Collaborating closely with ML Infra and Systems to push cluster utilization, throughput, and reliability - Turning raw robotic interaction data into generalizable model capabilities You might thrive in this role if you: - Have worked on large-scale ML systems with senior/staff (L6+) experience. - Have deep experience training large transformer or diffusion models at scale (for generative models e.g. including language models, audio models, or video models) - Have led or significantly contributed to multi-node, multi-GPU distributed training efforts - Have worked on scaling laws, optimization dynamics, and large-model failure modes - Have strong PyTorch fundamentals and comfort debugging at every layer of the stack - Care about both empirical rigor and raw iteration speed - Are excited about building general-purpose robot intelligence from first principles
Member of Technical Staff, Research
ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. THE ROLE: As a Member of Technical Staff on the Research team, you’ll push the boundaries of generative AI, advancing LLMs and multimodal systems through foundational research. Your work will enhance model efficiency, accuracy, and scalability, directly shaping our high-performance AI infrastructure. You'll collaborate with top experts in deep learning, distributed systems, and optimization to bring cutting-edge research into real-world applications. You'll also have the opportunity to shape how some of the world’s leading companies build and deploy AI through the models and tools you help create. KEY RESPONSIBILITIES - Conduct foundational research to advance the capabilities, efficiency, and reliability of LLMs and multimodal systems - Design, implement, and evaluate novel model architectures, training methods, and optimization techniques - Collaborate with engineering teams to transition research prototypes into production-grade systems - Analyze empirical results, identify performance bottlenecks, and iterate quickly to improve model quality - Contribute to internal research strategy by identifying high-impact opportunities and emerging trends in AI MINIMUM QUALIFICATIONS: - Research background in Artificial Intelligence, Machine Learning, Physics, or similar field - Experience solving analytical problems using analytic and quantitative approaches - Experience communicating research to audiences with different backgrounds - Experience coding in C/C++, Python, or other similar languages PREFERRED QUALIFICATIONS: - PhD degree in Computer Science, Computational Physics, Mathematics, or a similar field - Research and engineering experience demonstrated via grants, fellowships, patents, internships, work experience, and/or coding competitions - Experience having first-authored publications at peer-reviewed conferences or journals - Experience working with ML frameworks such as PyTorch, TensorFlow, or Jax WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Member of Technical Staff, LLM Infrastructure
ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. THE ROLE: As a Software Engineer on our AI Infrastructure team, you will help design the core systems that power Fireworks AI’s generative AI platform. You will help build infrastructure and tools that ensure the reliability, performance, quality, and availability of our AI system. Our mission is to make Fireworks AI the most reliable and user friendly generative AI platform in the world. You will partner closely with our cloud infrastructure team, product team, and performance team to deliver infrastructure that bridges the gap between our customer and the ultra-performant proprietary Fireworks inference engine. KEY RESPONSIBILITIES: - Contribute to the design and development of scalable backend infrastructure that supports distributed training, inference, and data pipelines - Build and maintain core backend services such as LLM CI/CD pipeline, control plane, and model serving systems - Support performance optimization, cost efficiency, and reliability improvements across compute, storage, and networking layers - Building frameworks and safeguards to ensure Fireworks AI has the best model quality in the industry - Collaborate with performance, training, and product teams to translate research and product needs into infrastructure solutions - Participate in code reviews, technical discussions, and continuous integration and deployment processes MINIMUM QUALIFICATIONS: - Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience) - 3 years of experience in software engineering, with a focus on infrastructure or machine learning systems - Strong programming skills in Python, Go, or a similar language - Proven experience in ML infrastructure and tooling (e.g., PyTorch, MLflow, Vertex AI, SageMaker, Kubernetes, etc.). - Basic understanding of LLM knowledge (e.g., context length, disaggregated prefill, KV cache memory estimation, etc) PREFERRED QUALIFICATIONS: - 5+ years of experience in software engineering, with a focus on infrastructure or machine learning systems - Experience with open source inference engine like vLLM, Sglang, or TRT-LLM - Contributions to open-source infrastructure or ML projects - Experience in building large scale ML/MLOps infrastructure WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Applied Machine Learning Engineer, Singapore
ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. THE ROLE: As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications. Your work will focus on developing, fine-tuning, and operationalizing machine learning models that drive business value and enhance user experiences. This is a hands-on engineering role that combines deep technical expertise with a strong customer focus to deliver scalable AI solutions. KEY RESPONSIBILITIES: - Customer Success: Collaborate directly with the GTM team (Account Executives and Solutions Architects) to ensure smooth integration and successful deployment of ML solutions. - Demo / Proof of Concept (PoC): Build and present compelling PoCs that demonstrate the capabilities of our AI technology. - Application Build: Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs. - Platform Features / Bug Fixes: Contribute to the internal ML platform, including adding features and resolving issues. - New Model Enablements: Integrate and enable new machine learning models into the existing platform or client environments. - Performance Optimizations: Improve system performance, efficiency, and scalability of deployed models and applications. - Partnership Enablement: Work closely with partners to enable joint AI solutions and ensure seamless collaboration. MINIMUM QUALIFICATIONS: - Bachelor’s degree in Computer Science, Engineering, or a related technical field. - 5+ years of experience in a software engineering role, with a strong preference for customer-facing roles. - Robust coding skills required, preferably with proficiency in Python. - Demonstrated ability to lead and execute complex technical projects with a focus on customer success. - Strong interpersonal and communication skills; ability to thrive in dynamic, cross-functional teams. PREFERRED QUALIFICATIONS: - Master’s degree in Computer Science, Engineering, or a related technical field. - Experience working in a startup or fast-paced environment. - Hands-on experience fine-tuning machine learning models, including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF or RFT). - Solid understanding of generative AI, machine learning principles, and enterprise infrastructure. WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Applied Machine Learning Engineer
ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. THE ROLE: As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications. Your work will focus on developing, fine-tuning, and operationalizing machine learning models that drive business value and enhance user experiences. This is a hands-on engineering role that combines deep technical expertise with a strong customer focus to deliver scalable AI solutions. KEY RESPONSIBILITIES: - Customer Success: Collaborate directly with the GTM team (Account Executives and Solutions Architects) to ensure smooth integration and successful deployment of ML solutions. - Demo / Proof of Concept (PoC): Build and present compelling PoCs that demonstrate the capabilities of our AI technology. - Application Build: Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs. - Platform Features / Bug Fixes: Contribute to the internal ML platform, including adding features and resolving issues. - New Model Enablements: Integrate and enable new machine learning models into the existing platform or client environments. - Performance Optimizations: Improve system performance, efficiency, and scalability of deployed models and applications. - Partnership Enablement: Work closely with partners to enable joint AI solutions and ensure seamless collaboration. MINIMUM QUALIFICATIONS: - Bachelor’s degree in Computer Science, Engineering, or a related technical field. - 5+ years of experience in a software engineering role, with a strong preference for customer-facing roles. - Robust coding skills required, preferably with proficiency in Python. - Demonstrated ability to lead and execute complex technical projects with a focus on customer success. - Strong interpersonal and communication skills; ability to thrive in dynamic, cross-functional teams. PREFERRED QUALIFICATIONS: - Master’s degree in Computer Science, Engineering, or a related technical field. - Experience working in a startup or fast-paced environment. - Hands-on experience fine-tuning machine learning models, including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF or RFT). - Solid understanding of generative AI, machine learning principles, and enterprise infrastructure. WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
AI Deployment Strategist
ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. ABOUT THE ROLE Fireworks AI is looking for an AI Deployment Strategist to be the technical backbone of our relationship with customers building on our fast inference platform. You'll be the connective tissue between customer engineering teams and Fireworks' product, engineering, and applied AI teams — driving successful onboarding, deep technical adoption, and long-term account health for companies running production AI workloads at scale. This is a hybrid technical/commercial role. You'll need enough depth to debug a customer's inference pipeline, discuss quantization tradeoffs, or advise on model routing and fine-tuning strategy — and enough business judgment to run a QBR, spot expansion opportunity, and manage renewal risk before it becomes a problem. Think of it as "product manager for the customer's problem": you own the why and the what — scoping the business case, defining success criteria, and managing the relationship end-to-end. You're embedded with your accounts from early technical evaluation through production and beyond. WHAT YOU'LL DO Deployment Strategy & Value Scoping - Partner with Sales during late-stage deals and early post-sale to map the customer's technical and organizational landscape — who the real stakeholders are, what "success" looks like, and where the risk sits - Quantify the business case for the deployment (cost-to-serve, latency/quality targets, ROI vs. the customer's current approach) and define a scoped pilot with clear milestones and exit criteria - Author the internal scoping brief that AI Field Engineering builds against — you own the "why" and "what," they own the "how" Onboarding & Technical Deployment - Own the post-sale technical relationship from kickoff through go-live, including model deployment, integration architecture, SSO/security configuration, and performance benchmarking - Partner with AI Field Engineering to deliver the successful transition from PoC to production - Build and execute joint success plans with clear milestones, ownership, and timelines Trusted Advisor & Adoption - Serve as the primary technical point of contact for a portfolio of strategic accounts, building deep relationships with engineering leaders, ML/platform teams, and power users - Advise customers on model selection, fine-tuning, prompt engineering, latency/cost optimization, and agent architecture as their usage matures - Drive usage against business objectives — not just technical enablement, but measurable outcomes (latency SLAs, cost per token, model quality, uptime) Troubleshooting & Escalation Management - Act as the technical escalation point for production issues, coordinating with Engineering and Support to drive resolution - Maintain runbooks and playbooks that reduce time-to-resolution across the account portfolio Voice of the Customer - Synthesize patterns across your accounts and feed them into product and engineering roadmaps - Represent customer priorities in internal planning, particularly around model support, tooling gaps, and platform reliability - Track a tight feedback loop between what customers are building and what Fireworks ships next Expansion, Renewal & Business Reviews - Own the technical narrative for quarterly/executive business reviews (QBRs/EBRs), including adoption trends, ROI, and roadmap alignment - Partner with the Account Executive on renewal strategy and identify expansion opportunities tied to new use cases, teams, or workloads - Forecast and proactively flag account health risks before they threaten retention WHAT WE'RE LOOKING FOR - Bachelor's degree required - 4+ years in a technical, customer-facing role: Deployment Strategy, Technical Account Management, Solutions Engineering, Forward-Deployed Engineering, or Technical Customer Success at an enterprise software or AI/ML company - Strong technical foundation: comfortable with APIs, cloud infrastructure (AWS/GCP/Azure), and enough hands-on coding ability to debug integrations, not just describe them - Direct experience with LLMs in production — understanding of probabilistic model behavior, prompt engineering, fine-tuning, RAG, and/or agent architectures - Track record of managing enterprise relationships end-to-end: technical delivery, executive communication, and commercial outcomes (renewal/expansion) - Excellent written and verbal communication — able to translate between ML engineers and business stakeholders fluently - Comfortable owning ambiguity in a fast-moving, early-stage function; you'll be shaping the playbook for this role at Fireworks, not just following one - Experience scoping high-value engagements — value-based/TCO-ROI framing, defining pilot success criteria, writing scoping docs or PRDs — gained in a Deployment Strategist, Forward-Deployed Engineer, or similar "product manager for the field" capacity is a strong plus - Bonus: experience with inference optimization, model serving infrastructure, or open-source model ecosystems (Llama, Mixtral, DeepSeek, etc.) WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Senior Research Scientist - AI Safety
WHY FACULTY? We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we’ve worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here https://faculty.ai/impact. We don’t chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence. Our business, and reputation, is growing fast and we’re always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology. AI is an epoch-defining technology, join a company where you’ll be empowered to envision its most powerful applications, and to make them happen. ABOUT THE TEAM We are the central research and development team driving Faculty's broader AI safety charter. We pioneer fundamental and applied technical AI safety research, producing rigorous scientific outputs—including publications, tooling, and technical reports—that advance the theory and practice of AI safety and directly shape the work of frontier AI labs, government agencies, and national security institutes. Our research spans both black-box and white-box approaches to understanding and steering AI systems. We care deeply about interpretability, faithfulness, and principled mitigation strategies, with a particular focus on the mechanistic and behavioural study of transformer-based large language models and long-running agentic systems. We operate at the cutting edge of the field, with current research threads including: - System Understanding: Interpretability, steering vectors, and the science of evaluations. - Control & Alignment: Goal drift, machine unlearning, and uncertainty calibration. - Societal Safeguards: Misinformation mitigation and robust deployment boundaries. We collaborate closely with the Faculty's wider AI safety team, a group with a well-established track record in capability evaluations and red-teaming for misuse risk across CBRN, cybersecurity, and international security. That work has been conducted for several leading frontier model developers and national safety institutes, and has been featured in OpenAI's o1 system card and Meta Muse Spark’s Safety & Preparedness Report. ABOUT THE ROLE As a Senior Research Scientist at Faculty, you will join our high-impact R&D team to lead original technical research that advances our understanding of safe AI systems. Operating with high agency, you will balance scientific rigour with practical relevance to shape the future of safe AI deployment. Working closely with our broader AI safety practice, your scientific outputs—ranging from peer-reviewed publications to technical tooling—will directly inform frontier labs and government institutes, driving our entrepreneurial ambition to build a safer technological future. WHAT YOU'LL BE DOING: - Leading original technical research in AI safety, steering projects from initial research question to high-impact publication. - Shaping the research agenda by identifying high-impact projects that balance scientific rigour with real-world relevance. - Contributing thought leadership and deep technical expertise to support wider evaluation and red-teaming efforts. - Supporting our positioning as a global leader through active external engagement with frontier labs and government stakeholders. - Developing robust, reproducible research code and engineering infrastructure to accelerate our scientific discovery. - Collaborating with cross-functional safety teams to translate fundamental research into practical mitigation strategies. WHO WE'RE LOOKING FOR: - Strong track record in AI research, evidenced by publications or equivalent experience, alongside the ability to tackle open, unsolved problems. - Capability to own research end-to-end, moving independently from framing novel problems to generating high-quality research artifacts. - Passion for technical AI safety, whether developed in academia, industry labs, or evaluation roles. - Deep knowledge of language models and generative AI architectures, including white-box techniques beyond API-level access. - Strong foundation in machine learning fundamentals, particularly in robustness, explainability, or uncertainty estimation. - Proficiency with deep learning frameworks like PyTorch and the Python skills required to build robust research tooling. OUR RECRUITMENT ETHOS We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We’re united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations. If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours. A note on AI: we're happy for you to use it for research and interview prep, but please don't use it to generate answers during live interviews. We also use an AI note-taker (Metaview) in interviews so interviewers can stay present (which you can opt out of just let us know,) and every application is reviewed by a human, never decided by AI.
Research Scientist - Machine Learning
Extropic’s hardware massively accelerates certain kinds of probabilistic inference. Our ML team works on the science of training models in the thermodynamic paradigm, and we are looking for senior research and engineering talent to derive probabilistic ML theory, empirically demonstrate its scaling properties, and deploy performant models. Senior hires will be leading their own research direction and are therefore expected to quickly become experts across our abstraction stack, including the hardware, software, physics, and math. RESPONSIBILITIES - Collaborate with senior researchers, residents, engineers, and physicists to derive the theory of new probabilistic models and their learning rules, including energy-based models and diffusion models - Scale up experimentation infrastructure and optimize over the design space of models - Implement, visualize, and evaluate new architectures, training algorithms, and benchmarks - Publish papers, contribute to open source, and communicate design insights to our hardware team - Create production models for domain experts using customer data REQUIRED QUALIFICATIONS - Experience in scientific Python and at least one deep learning framework (PyTorch, JAX, TensorFlow, Keras) - Extremely strong foundations in probability and linear algebra - Familiarity with deep learning theory and literature, including theory of over-parameterization and scaling laws - Publications in top ML conferences (NeurIPS, ICML, ICLR, CVPR) - Experience training high-performance models, including familiarity with infrastructure (Slurm, Ray, Weights & Biases) - Experience deploying models, including familiarity with infrastructure (Ray, AWS, ONNX) PREFERRED QUALIFICATIONS - Experience designing probabilistic graphical models (PGM) - Experience training energy-based models (EBMs) or diffusion models - Experience with numerical methods in diffeq solvers - Experience with message passing or training graph neural networks (GNNs) - Strong theoretical background in information geometry - Strong theoretical background in random matrix theory - Strong grasp of computational Bayesian methods, including MCMC sampling methods and variational inference This position will require access to information subject to control under U.S. export control laws and regulations, including the Export Administration Regulations (“EAR”). Please note that any offer for employment will be conditioned on authorization to receive controlled items.
Sr. Developer Advocate, AI and Machine Learning
RDQ427R542 At Databricks, we are passionate about enabling data teams to solve the world's most challenging problems — from making the next mode of transportation a reality to accelerating medical breakthroughs. We do this by building and running the world's best data and AI platform, enabling our customers to use deep data insights to improve their businesses. Our ability to execute this mission depends on building trust and recognition within an ever-growing community of data engineers, analysts, scientists, machine learning practitioners, and AI practitioners. Are you the person who is already at every AI meetup in the Bay Area — not to be seen, but because that's where the interesting conversations are happening? Are you a hands-on practitioner in generative AI, agents, and MLOps who can hold your own in a room full of AI engineers and leave them with something they can actually use? As a Senior Developer Advocate for AI and Machine Learning, you'll be the voice of Databricks in the Bay Area AI community and a crucial link between our engineering teams and the global community of data scientists and AI engineers. You'll focus on how practitioners build, evaluate, govern, and ship agentic systems on the Databricks Data + AI Platform — with particular depth in Genie Agents, Genie Code, Genie One, MLflow, and Databricks Unity AI Gateway. You'll translate what you learn from customers and the community into reference implementations, demos, and content that are production-ready rather than merely impressive on stage. This is a role for someone who wants to be in the room. San Francisco and the wider Bay Area are the densest concentration of AI practitioners in the world, and we want Databricks to be present and useful in that community — at meetups, hack nights, demo nights, founder dinners, and the hallway conversations around them. You'll build genuine relationships with the people building the future of AI here, and you'll bring what you hear back to our product and engineering teams. Your responsibilities will encompass a wide range of knowledge-sharing activities. You'll deliver engaging talks, host panels and meetups, create blogs and video content, develop courseware, answer questions in community forums, and meet one-on-one with influential AI engineers and data scientists. Reporting to the Head of Developer Relations, you'll collaborate with fellow Developer Advocates and program managers to create and execute a cohesive and impactful developer relations strategy. The ideal candidate embodies the values of our Developer Relations team: a deep passion for data and AI, genuine empathy for developers' needs, and a strong commitment to explaining our products clearly and honestly. More about the DevRel team The Developer Relations (DevRel) team at Databricks is dedicated to building and fostering relationships with communities of data practitioners. Our goal is to drive awareness and adoption of the Databricks Data + AI Platform — including Lakeflow, Lakebase, Genie Agents, and AI/BI — alongside the open source projects that underpin it, such as Apache Spark™, Delta Lake, Unity Catalog, MLflow, Apache Iceberg™, and Omnigent. Our team also owns programs like the Databricks MVP program, the user group program, and DevConnect roadshows. The impact you will have - Be a consistent, recognized presence in the Bay Area AI community — speaking at local user groups and meetups, showing up to the events that matter, and building real relationships with the practitioners and founders shaping the space. - Own medium-to-large advocacy projects end-to-end, from scoping through delivery, for the AI and agents area. - Create production-ready reference implementations, starter kits, and demos that show practitioners how to build, evaluate, and govern agents on Databricks — and get them to first value in minutes, not hours. - Create high-quality educational content — videos, sample notebooks, datasets, tutorials, courseware, and blog posts — and reuse each core asset across formats to reach practitioners wherever they already are. - Drive awareness and adoption of Databricks AI capabilities through speaking engagements at industry events and conferences. - Act as a trusted advisor between the AI community and our product teams, and influence roadmap discussions with what you learn. - Expand and nurture the Databricks AI and ML community by growing meetups and user groups and supporting practitioners in online communities. - Gather and analyze community feedback to drive continuous improvement of Databricks products and services. - Mentor and unblock less-experienced advocates in content, demos, and public speaking. What we look for - 5+ years of combined experience as a developer advocate and in a hands-on technical role — software engineer, ML engineer, data scientist, or solutions architect. - Professional experience actually building and operating AI or ML systems, not only presenting about them. - Subject matter knowledge of solving AI and ML problems at scale, including agent design, evaluation, and production operations. - Comprehensive understanding of the Databricks AI ecosystem, including Genie Agents, Genie, MLflow, and Databricks Unity AI Gateway. - Strong Python skills and fluency with the current agent and ML tooling landscape. - Based in the Bay Area and genuinely willing to be out in the community on a regular basis, including evenings. - Active participation and recognized leadership in AI and ML community forums, chats, and meetups — measured by what the community builds with your help, not by follower count. - A public portfolio of technical work: tutorials, talks, repositories, or sustained community contributions. - Proven track record in nurturing developer communities, organizing user groups, and facilitating meetups. - Exceptional communication skills, with a talent for articulating complex concepts through writing, teaching, video, and public speaking. - Deep empathy for developer needs, with the ability to craft engaging experiences across tutorials, notebooks, and community platforms. - Adept at collaborating with cross-functional stakeholders to align community initiatives with product objectives. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . Zone 2 Pay Range $133,000 - $186,100 USD About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Staff Tech Lead Manager, Machine Learning, Vision...
Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ ABOUT OUR ENGINEERING TEAMS Wayve’s engineering teams are building the AI, robotics, simulation, data, and systems foundations needed to deploy a generalisable AI Driver safely and at scale. ABOUT THE MEASUREMENT TEAM The Measurement team within AI Evaluation builds and productionises the offline scene-understanding models that measure driving performance after on-road runs and in simulation. The team adapts on-vehicle and foundation-model technology to exploit the greater compute, model capacity, and temporal context available offline. 🧠 YOUR DAY-TO-DAY - Lead and develop a London-based team of Senior Machine Learning Engineers while remaining deeply engaged in technical direction. - Set the architecture for robust, scalable offline scene-understanding and measurement models. - Translate ambiguous business goals into sprint, quarterly, annual, and multi-year technical roadmaps. - Review designs and code, raise the engineering quality bar, and ensure models are reliable enough for customer-facing deliverables. - Align roadmaps across on-vehicle modelling, foundation models, Evaluation, Simulation, and Model Development Platform teams in the UK and US. - Identify capability gaps ahead of demand and build the technical and organisational case to address them. 🧩 WHAT YOU’LL BE WORKING ON - Offline models that predict and explain counterfactual driving outcomes. - Scene-understanding systems that assess coverage, mine rare events, and measure learned driving behaviour. - Rig-agnostic architectures built from transformer, multimodal, and foundation-model technology. - Production ML systems that shorten the loop between driving-model changes and reliable evaluation evidence. - A growing senior team operating across multiple organisations and locations. 🙌 YOU SHOULD APPLY IF - You have 8+ years in ML engineering, including hands-on computer vision with camera or lidar data and a record of shipping production ML systems. - You have 2+ years managing or tech-leading senior engineers and enjoy combining people leadership with technical leadership. - You bring staff-level depth in transformers, multimodal systems, foundation models, and large-scale training. - You are proficient in Python and PyTorch or a similar framework and have strong judgement about production-grade ML. - You can align roadmaps across teams and geographies and communicate technical strategy clearly to senior stakeholders. - Experience with 3D scene understanding, offline models, simulation, counterfactual evaluation, or distributed teams is beneficial. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. MORE ABOUT WAYVE 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience, developing the ability to adapt, generalise and improve at scale. Our ambition is to make autonomy universal. Wayve’s mapless and hardware-agnostic AI platform integrates with global OEM partners, enabling continuous software evolution from L2+ through to L4. HOW WE WORK 💻 — LOCATIONS & FLEXIBLE WORKING This role is based in London. We operate a hybrid model combining in-person collaboration in our offices and workshops with focused time working remotely. 🔍 THE INTERVIEW PROCESS - Initial call / recruiter screen (25 min) - Hiring Manager Domain Chat (60 min) - Deep-dive interviews covering ML architecture, systems, and domain expertise (4 hours) - Final interview focused on mission and values alignment (45 min) We’ll always explain the format and work around your availability. WHAT’S IN IT FOR YOU (LOCATION DEPENDENT) - 💰 Salaries benchmarked against the market annually - 📈 Meaningful equity - ✈️ Relocation support and visa sponsorship where applicable - ✅ Hybrid working and access to vehicle workshops and labs - 📚 Learning and development support - 🩺 Comprehensive location-dependent health, family, pension, and wellbeing benefits A quick, honest note before you apply. Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one. If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. For more information visit Careers at Wayve. https://wayve.ai/careers/ To learn more about what drives us, visit Values at Wayve https://wayve.ai/careers/ DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.
Sr Staff/Principal Laser Design Engineer
Lightmatter is leading the revolution in AI data center infrastructure, enabling the next giant leaps in human progress. The company invented the world’s first 3D-stacked photonics engine, Passage™, capable of connecting thousands to millions of processors at the speed of light in extreme-scale data centers for the most advanced AI and HPC workloads. Lightmatter raised $400 million in its Series D round, reaching a valuation of $4.4 billion. We will continue to accelerate the development of data center photonics and grow every department at Lightmatter! If you're passionate about tackling complex challenges, making an impact, and being an expert in your craft, join our team of brilliant scientists, engineers, and accomplished industry leaders. Lightmatter is (re)inventing the future of computing with light! We are hiring a Laser Design Engineer to join our laser module development team. In this role, you will own the design of high-power laser epitaxy and laser devices—including DFB, DBR, and external-cavity architectures—for Lightmatter’s integrated and highly scalable laser solutions for AI communications and computation. You will drive designs from epitaxial layer stack through cavity and device architecture, working closely with colleagues and cross-disciplinary engineering teams internally and across our ecosystem of manufacturing partners to translate system requirements into robust, manufacturable, high-reliability hardware. This role requires deep expertise in semiconductor laser and epitaxy design, a strong command of laser and semiconductor device physics, and the judgment to navigate the design space of performance, schedule, reliability, power consumption, and cost. As a senior technical anchor, you will plan and execute across multiple concurrent programs, resolve critical and often ambiguous technical challenges with limited oversight, develop new methods and design approaches, and serve as a recognized internal subject-matter expert. Creativity, analytical rigor, and clear communication are essential. Join a tight-knit team where each individual’s contributions directly influence the success of the company and product. You’ll have the opportunity to build innovative laser solutions from the ground up and tackle groundbreaking challenges. Work with people who love to build and who thrive in diverse technical environments where great ideas are prioritized. Responsibilities - Own the design of high-power laser epitaxial structures and laser devices—including DFB, DBR, and external-cavity architectures. - Design epitaxial layer stacks in III-V material systems, optimizing for optical gain, carrier confinement, threshold current density, wall-plug efficiency, and high-temperature performance. - Optimize laser cavity designs to achieve target output power, precise wavelength control, narrow linewidth, high side-mode suppression ratio (SMSR), low relative intensity noise (RIN), and robust back-reflection tolerance. - Contribute to monolithic and silicon-photonics heterogeneously integrated laser and semiconductor optical amplifier (SOA) development. - Use physics-based and traveling-wave simulation tools to model heterostructure and cavity behavior, spectral response, and tuning, translating high-level system needs into detailed device designs. - Partner with the characterization team to define test beds and map measured L-I-V curves, near-/far-field profiles, thermal coefficients, and linewidth back to design parameters, iterating to close performance gaps. - Collaborate across disciplines—systems, analog/electrical, packaging, and thermal engineering—to define component-level specs derived from link budgets, co-design drivers and submounts, and define optical coupling and thermal-management strategies. - Work with epitaxy and foundry vendors to specify, qualify, and transfer designs into volume manufacturing. - Act as an internal subject-matter expert and thought leader on laser device and epi design; mentor engineers, review designs, and contribute to the laser technology roadmap for performance, scalability, power, and cost. - Publish and present novel ideas, and participate in premier photonics conferences. Requirements - A Ph.D. in Photonics, Electrical Engineering, Applied Physics, Materials Science, or a related discipline with at least 5 years of relevant post-degree experience; or an M.S. with at least 8 years of relevant experience; or equivalent experience. - Deep, hands-on expertise in semiconductor laser and epitaxy design for III-V compound semiconductor devices. - Proven track record designing DFB, DBR, and/or external-cavity lasers. - Strong grounding in laser physics and semiconductor device physics (optical gain, threshold, slope efficiency, RIN, linewidth, thermal rollover, and back-reflection behavior). - Proficiency with physics-based laser and photonic device simulation tools (e.g., PhotonDesign Harold/PICWave, VPIphotonics, Lumerical, or equivalent) and data analysis in Python. - Demonstrated ability to independently own complex designs spanning multiple programs, and to resolve critical, ambiguous technical issues as a recognized subject-matter expert. - Track record of close collaboration with characterization/test teams and cross-functional hardware teams. - Ability to convey complex technical concepts to both technical and non-technical stakeholders with strong and clear communication. Preferred Qualifications - Experience with silicon photonics and heterogeneous integration of III-V gain material on silicon. - Experience designing semiconductor optical amplifiers (SOAs). - Experience with high power semiconductor lasers for datacom applications. - Experience maturing a laser product from prototype through high-volume manufacturing, including epi growth and reliability qualification. - A record of publishing novel photonics results and engaging with the broader optics community. - Willing and able to learn quickly, self-starter with a “no task is too big or small” attitude. We offer competitive compensation. The base salary range for this role determined based on location, experience, educational background, and market data. Salary Range: total compensation goes beyond base salary, it also includes a new hire equity grant, annual performance-based equity, and other rewards that recognize your impact and contribution. $212,000 - $320,000 USD Benefits - Comprehensive Health Care Plan (Medical, Dental & Vision) - Retirement Savings Matching Program - Life Insurance (Basic, Voluntary & AD&D) - Generous Time Off (Vacation, Sick & Public Holidays) - Paid Family Leave - Short Term & Long Term Disability - Training & Development - Commuter Benefits - Flexible, hybrid workplace model - Equity grants (applicable to full-time employees) Benefits eligibility may vary depending on your employment status and location. Lightmatter recruits, employs, trains, compensates, and promotes regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law. Export Control Candidates should have capacity to comply with the federally mandated requirements of U.S. export control laws.
Senior Photonics Design Automation Engineer
Lightmatter is leading the revolution in AI data center infrastructure, enabling the next giant leaps in human progress. The company invented the world’s first 3D-stacked photonics engine, Passage™, capable of connecting thousands to millions of processors at the speed of light in extreme-scale data centers for the most advanced AI and HPC workloads. Lightmatter raised $400 million in its Series D round, reaching a valuation of $4.4 billion. We will continue to accelerate the development of data center photonics and grow every department at Lightmatter! If you're passionate about tackling complex challenges, making an impact, and being an expert in your craft, join our team of brilliant scientists, engineers, and accomplished industry leaders. Lightmatter is (re)inventing the future of computing with light! We are hiring a Photonics PDK Design Automation Engineer to join our team. In this role, you will develop our internal Process Design Kit (PDK). You will act as the architect of our component library, bridging the gap between photonics device design and system-level verification. You will develop robust flow ensuring that our simulation environment accurately predicts the behavior of our physical layouts. You will work heavily in Python, EDA tools, applying software engineering rigor to the challenges of photonic hardware design. Responsibilities - Develop and maintain a library of pcells using Python-based layout frameworks. Ensure strict synchronization between physical layout, logical netlists, and behavioral models. - Develop and maintain custom photonic Layout vs Schematic (LVS) decks across multiple foundry technologies - Design and test device and parameter extraction for innovative photonic devices - Troubleshoot and resolve LVS mismatches in complex analog and photonic circuits - Actively collaborate with design, layout, modeling, and infrastructure engineers to deliver high impact tape-outs - Automated Verification: Implement regression testing and Continuous Integration (CI) pipelines to ensure PDK updates do not break legacy designs or connectivity logic. - Contribute to other PDK, DRC, and automation projects as needed Qualifications - Masters or PhD in Electrical Engineering, Computer Engineering, Photonics, or related field. - Prior experience in performing physical layout using foundry-provided process design kits (PDKs), adhering to specific design rules and constraints. - Experience with photonics layout - Python based layout experience. Comfortable with object-oriented programming and scientific libraries (NumPy, SciPy). - Experience writing software and scripts in languages such as Python, SVRF, Tcl, Bash, and SKILL - In-depth knowledge of integrated photonic devices, analog devices, and parasitics - In-depth knowledge of foundry process (SOI, etching, implants, metal stacks) - Proficiency with Linux environments, advanced version control (Git), and software engineering best practices - Demonstrated strong problem solving skills for problems without obvious solutions Preferred Qualifications - 1+ years experience in LVS deck development for Siemens Calibre, Cadence Pegasus, and/or Synopsys IC Validator, experience troubleshooting and resolving LVS mismatches in complex circuit designs - Proficiency with python-based layout tools such as gdstk, GDSFactory, and KLayout - Experience with Git setup, version control, unit testing (pytest), and CI/CD workflows. - Experience with DRC deck and rule development - Experience working on large-scale designs using industry standard environments such as Cadence Virtuoso - Excellent communication skills, with a demonstrated ability to collaborate effectively with multidisciplinary teams - Strong project execution skills, with the ability to handle multiple priorities and meet project milestones We offer competitive compensation. The base salary range for this role determined based on location, experience, educational background, and market data. Salary Range: total compensation goes beyond base salary, it also includes a new hire equity grant, annual performance-based equity, and other rewards that recognize your impact and contribution. $165,000 - $200,000 USD Benefits - Comprehensive Health Care Plan (Medical, Dental & Vision) - Retirement Savings Matching Program - Life Insurance (Basic, Voluntary & AD&D) - Generous Time Off (Vacation, Sick & Public Holidays) - Paid Family Leave - Short Term & Long Term Disability - Training & Development - Commuter Benefits - Flexible, hybrid workplace model - Equity grants (applicable to full-time employees) Benefits eligibility may vary depending on your employment status and location. Lightmatter recruits, employs, trains, compensates, and promotes regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law. Export Control Candidates should have capacity to comply with the federally mandated requirements of U.S. export control laws.
Principal Field Application Engineer
Lightmatter is leading the revolution in AI data center infrastructure, enabling the next giant leaps in human progress. The company invented the world’s first 3D-stacked photonics engine, Passage™, capable of connecting thousands to millions of processors at the speed of light in extreme-scale data centers for the most advanced AI and HPC workloads. Lightmatter raised $400 million in its Series D round, reaching a valuation of $4.4 billion. We will continue to accelerate the development of data center photonics and grow every department at Lightmatter! If you're passionate about tackling complex challenges, making an impact, and being an expert in your craft, join our team of brilliant scientists, engineers, and accomplished industry leaders. Lightmatter is (re)inventing the future of computing with light! We are now looking for an outstanding Field Application Engineer - High-speed I/O Specialist to join our team. The role’s primary responsibility is to work collaboratively with customers in architecting the next generation of devices enabled by Lightmatter’s photonic interconnect technology Passage. In this role, you will work closely with Tier 1 Semiconductor companies that are defining the next generation of silicon in the data center. You will be working cross functionally within Lightmatter as you define an optimal technical solution. Additionally, you will be collaborating with customers who are domain experts in high-speed serial links (SerDes) and protocols. You should be comfortable supporting deep technical discussions focused on high-speed serial I/O design. Excellent interpersonal skills and the ability to work independently are essential to being successful in the role. Responsibilities - Provide the full technical support of the customer HSIO design, including the early technical assessment, design scheme discussion, hardware design review, hardware debug support and other critical trouble shootings - Develop and deliver technical proposals to customers, based on customer’s technical requirements and system architecture - Work as the field expert on HSIO area, to provide the in-depth training and guidance of the HSIO design to the FAE team, to create and share the related technical articles and reference designs - Work collaboratively with Lightmatter Engineering in supporting customer design wins and communicating progress updates to customer engineering leads Requirements - BS or MS in Engineering, Mathematics, Physics, or Computer Science (or equivalent experience). - 10+ years of work related experience in a high-tech electronics industry with high-speed serial I/O design, including transceiver architecture, CDR, DFE or other - In-depth knowledge of the signal integrity, power integrity simulation and the related hardware design. Be familiar with the SI/PI EDA simulation tools and models, eg. Keysight ADS. - Customer facing skill-set and background as well as the ability to communicate effectively with customers - Action-oriented with strong analytical and problem-solving skills - Previous experience developing and delivering technical presentations to both executive management and cross-functional engineering teams and external clients - Built trusted working relationships across teams, improving coordination and execution speed - Experience working with silicon photonics and/or optical transceiver - In-depth knowledge of semiconductor and laser technology Preferred Qualifications - Familiar with the digital signal processing theory and the related tools, like Matlab Simulink. - Analog signal and mixed signal design, ADC, DAC design experience - C, C++ and other software experience - Experience working in customer facing role acting as the domain expert for silicon photonics/optics - Experience deploying or supporting customers who are deploying optics in a data center environment at scale We offer competitive compensation. On target earnings range is listed below. The final compensation will be determined based on location, experience, educational background, and market data. Sales Compensation $250,000 - $300,000 USD Benefits - Comprehensive Health Care Plan (Medical, Dental & Vision) - Retirement Savings Matching Program - Life Insurance (Basic, Voluntary & AD&D) - Generous Time Off (Vacation, Sick & Public Holidays) - Paid Family Leave - Short Term & Long Term Disability - Training & Development - Commuter Benefits - Flexible, hybrid workplace model - Equity grants (applicable to full-time employees) Benefits eligibility may vary depending on your employment status and location. Lightmatter recruits, employs, trains, compensates, and promotes regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law. Export Control Candidates should have capacity to comply with the federally mandated requirements of U.S. export control laws.
Photonics Hardware Validation Engineer
Lightmatter is leading the revolution in AI data center infrastructure, enabling the next giant leaps in human progress. The company invented the world’s first 3D-stacked photonics engine, Passage™, capable of connecting thousands to millions of processors at the speed of light in extreme-scale data centers for the most advanced AI and HPC workloads. Lightmatter raised $400 million in its Series D round, reaching a valuation of $4.4 billion. We will continue to accelerate the development of data center photonics and grow every department at Lightmatter! If you're passionate about tackling complex challenges, making an impact, and being an expert in your craft, join our team of brilliant scientists, engineers, and accomplished industry leaders. Lightmatter is (re)inventing the future of computing with light! In this role, you will design, develop, and build photonic test systems that are critical to the characterization and validation of our products. You will collaborate closely with device, hardware, software, and digital and analog design teams to support the end-to-end development, validation, and qualification of photonic systems and their key components. Join a tight-knit team where each individual’s contributions directly impact the success of the company and our innovative products. At Lightmatter, you’ll have the opportunity to work with people who love to build and thrive in technically diverse environments where great ideas are valued and prioritized. We are hiring a Photonics Hardware Validation Engineer to join our Validation team to drive photonics testing, characterization and validation of our silicon photonics-based optical Interconnect Products. Responsibilities - You will need to understand our chip specifications well so that you can take responsibility for chip bring-up, testing, and validation of our next-generation Optical Interconnect products. This includes supporting the development of chip validation plans aligned to product specifications, xDVT, and interoperability testing. - You will be required to contribute to test plan development, test bench configuration planning, setup of lab test equipment, and troubleshooting of validation setups to enable successful chip bring-up and testing. - You will work within the product validation team on electro-optic, link-level testing in our lab using industry-standard practices and equipment. Qualifications - Bachelor’s degree in Electrical Engineering, Physics, or a related field. - Hands-on experience with standard bench test equipment for data communications, including electro-optic link testing and characterization. - Experience in the data communications field, specifically in electro-optic link-level testing and characterization in a lab environment. - Familiarity with PCB design, including hardware testing and debugging of electro-optic circuits. - Ability to work closely with Design Engineers, Software Engineers, and broader cross-functional teams to quickly gain knowledge of Lightmatter products and drive execution of chip bring-up and validation in our lab. - Basic scripting skills for automation of lab tests including analysis of results and report generation. - Solid understanding of electrical and optical blocks in transmitters and receivers to enable efficient chip bring-up and link testing. - Ability to troubleshoot and debug the electrical optical building blocks in a transceiver to triage bugs and root-cause performance gaps. Preferred Qualifications: - PhD is a plus - Experience with high-speed data communication systems, including NRZ and PAM4 signaling. - Familiarity with industry standards such as IEEE, OIF-CEI, or similar for data communication systems. - Proficiency in Python, MATLAB, or similar tools for scripting and automation. - Strong documentation skills to develop clear test plans, reports, and technical presentations. - Prior experience in leading or mentoring junior engineers in lab validation and testing workflows. We offer competitive compensation. The base salary range for this role determined based on location, experience, educational background, and market data. Salary Range: total compensation goes beyond base salary, it also includes a new hire equity grant, annual performance-based equity, and other rewards that recognize your impact and contribution. $177,000 - $240,000 USD Benefits - Comprehensive Health Care Plan (Medical, Dental & Vision) - Retirement Savings Matching Program - Life Insurance (Basic, Voluntary & AD&D) - Generous Time Off (Vacation, Sick & Public Holidays) - Paid Family Leave - Short Term & Long Term Disability - Training & Development - Commuter Benefits - Flexible, hybrid workplace model - Equity grants (applicable to full-time employees) Benefits eligibility may vary depending on your employment status and location. Lightmatter recruits, employs, trains, compensates, and promotes regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law. Export Control Candidates should have capacity to comply with the federally mandated requirements of U.S. export control laws.
Photonics Architect
Lightmatter is leading the revolution in AI data center infrastructure, enabling the next giant leaps in human progress. The company invented the world’s first 3D-stacked photonics engine, Passage™, capable of connecting thousands to millions of processors at the speed of light in extreme-scale data centers for the most advanced AI and HPC workloads. Lightmatter raised $400 million in its Series D round, reaching a valuation of $4.4 billion. We will continue to accelerate the development of data center photonics and grow every department at Lightmatter! If you're passionate about tackling complex challenges, making an impact, and being an expert in your craft, join our team of brilliant scientists, engineers, and accomplished industry leaders. Lightmatter is (re)inventing the future of computing with light! Lightmatter builds chips that enable extreme-scale AI computing clusters. If you're a collaborative engineer or scientist who has a passion for innovation, solving challenging technical problems and doing impactful work, consider joining the team at Lightmatter! We are hiring a Photonics Architect. The selected candidate will partner with the internal engineering team to deliver groundbreaking products to the market. As an architect, you will provide detailed technical documents that describe what needs to be built to the engineering team. This will require a solid understanding of process technology, state of the art in photonics design, and clear communication skills. In this role, you will be defining the (micro-)architecture for a ground-breaking photonic-based communication fabric. From component-level design to top-level layout, you will be part of a team that enables high-bandwidth low-latency photonic interconnect/switch fabric. You will report directly to the Sr. Director of Architecture, but you will also work closely with our photonic and analog teams to define and optimize the features needed to improve the power, performance, and area (PPA) of the next generation product. Join a tight-knit team where each individual’s contributions directly influence the success of the company and product. You'll have the opportunity to build a new kind of computer from the ground up and to solve groundbreaking challenges along the way. Work with people who love to build and who thrive in technically diverse environments where great ideas are prioritized. Responsibilities: - Propose innovative photonic designs—modulators, receivers, multiplexers, lasers, amplifiers—for the product. - Work closely with the photonic engineering design team to provide the performance, power, and area estimates of the photonic devices within Passage. - Actively collaborate with the analog engineering team to provide a high-bandwidth photonic interconnect solution. - Author, review, and validate architectural specifications. Qualifications: - PhD in electrical engineering, physics, applied physics, or a discipline related to photonics. - In-depth knowledge of photonic devices and semiconductor physics. - Experience with integrated silicon photonics components. Preferably has experience with photonic simulation tools: FDTD, umerical, Verilog-A, VPI Photonics. - Ability and desire to collaborate in a cross-disciplinary team. - Demonstrated strong problem solving skills specifically pertaining to problems that do not have obvious solutions. We offer competitive compensation. The base salary range for this role determined based on location, experience, educational background, and market data. Salary Range: total compensation goes beyond base salary, it also includes a new hire equity grant, annual performance-based equity, and other rewards that recognize your impact and contribution. $118,000 - $171,000 CAD Benefits - Comprehensive Health Care Plan (Medical, Dental & Vision) - Retirement Savings Matching Program - Life Insurance (Basic, Voluntary & AD&D) - Generous Time Off (Vacation, Sick & Public Holidays) - Paid Family Leave - Short Term & Long Term Disability - Training & Development - Commuter Benefits - Flexible, hybrid workplace model - Equity grants (applicable to full-time employees) Benefits eligibility may vary depending on your employment status and location. Lightmatter recruits, employs, trains, compensates, and promotes regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law. Export Control Candidates should have capacity to comply with the federally mandated requirements of U.S. export control laws.
Director, Photonics Hardware Validation
Lightmatter is leading the revolution in AI data center infrastructure, enabling the next giant leaps in human progress. The company invented the world’s first 3D-stacked photonics engine, Passage™, capable of connecting thousands to millions of processors at the speed of light in extreme-scale data centers for the most advanced AI and HPC workloads. Lightmatter raised $400 million in its Series D round, reaching a valuation of $4.4 billion. We will continue to accelerate the development of data center photonics and grow every department at Lightmatter! If you're passionate about tackling complex challenges, making an impact, and being an expert in your craft, join our team of brilliant scientists, engineers, and accomplished industry leaders. Lightmatter is (re)inventing the future of computing with light! In this pivotal role within Silicon Engineering, you will own the end-to-end post-silicon validation strategy and execution across optical, electrical, and system level. You will collaborate directly with Silicon Design, Laser, Packaging, Software, and Supply Chain leadership to architect scalable lab infrastructure, drive automated testing frameworks, and ensure our products meet world-class reliability standards for hyperscale AI data centers. Responsibilities - Scale the product validation team by recruiting, mentoring, and developing multi-disciplinary talent across optical, electrical, software, and automation domains. - Drive silicon and platform validation strategies, requirements, and execution plans for our optical interconnect platform. - Architect and define comprehensive, at-scale validation environments, test frameworks, automated test suites, and telemetry data collection workflows. - Analyze silicon and system architecture to identify integration risks, design gaps, and marginality issues requiring rigorous corner validation. - Oversee the execution of validation plans, leading technical triage and root-cause debug for hardware, firmware, and system-level issues. - Partner closely with Silicon, Packaging, Hardware, and Software teams to align validation coverage with design specifications and system requirements. - Influence pre-silicon design and simulation milestones to ensure feature completeness, advocating for validation hooks, telemetry sensors, and diagnostic monitors on-chip and on reference platforms. - Drive hardware and software automation initiatives to enhance testing throughput, repeatability, and data analytics capabilities across validation labs. Qualifications - BS, MS, or Ph.D. in Electrical Engineering, Computer Engineering, Photonics, Physics, or a related technical field. - 12+ years of semiconductor or hardware validation experience, with significant exposure to optical/photonics link-level products. - Strong technical background in laser, optics, or silicon photonics architecture and characterization. - 5+ years of experience developing test content, automation infrastructure, and scripting in Python or C++. - Proven leadership track record managing cross-functional engineering teams in a fast-paced environment working on transceivers or link-level products. - Solid understanding of industry standard requirements (IEEE, MSA, OIF) and product validation through corner testing (temperature, voltage, and process matrices), including defining validation plans and generating validation reports. - Demonstrated ability to establish process improvements, test methodologies, and data analysis workflows. Preferred Qualifications - Excellent technical leadership, communication, and cross-functional project management skills. - Proven track record of successfully leading multiple link-level products through validation and high-volume production release. - Experience scaling engineering teams through hypergrowth, establishing career development frameworks, and fostering a high-performance environment. - Demonstrated capability to align executive stakeholders on technical roadmaps, resource trade-offs, and risk mitigation strategies. We offer competitive compensation. The base salary range for this role determined based on location, experience, educational background, and market data. Salary Range: total compensation goes beyond base salary, it also includes a new hire equity grant, annual performance-based equity, and other rewards that recognize your impact and contribution. $261,000 - $300,000 USD Benefits - Comprehensive Health Care Plan (Medical, Dental & Vision) - Retirement Savings Matching Program - Life Insurance (Basic, Voluntary & AD&D) - Generous Time Off (Vacation, Sick & Public Holidays) - Paid Family Leave - Short Term & Long Term Disability - Training & Development - Commuter Benefits - Flexible, hybrid workplace model - Equity grants (applicable to full-time employees) Benefits eligibility may vary depending on your employment status and location. Lightmatter recruits, employs, trains, compensates, and promotes regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law. Export Control Candidates should have capacity to comply with the federally mandated requirements of U.S. export control laws.
Senior Computer Vision Engineer, Space
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. ABOUT THE TEAM Anduril's Space team is dedicated to expanding our AI-powered capabilities into the final frontier, enhancing Space Domain Awareness, Space Control, and Command and Control for U.S. military and allied partners. We're developing fully integrated hardware and software systems, including Lattice for Space Missions and modular payloads, to address growing threats in space and ensure our Guardians maintain a decisive advantage in this contested warfighting domain. ABOUT THE JOB We are looking for a Senior Computer Vision Engineer to join our rapidly growing team in Washington DC. In this role, you will be responsible for working on and understand the design of all perception subsystems to include but not limited to hardware sensors and advanced processing platforms, navigation algorithms, flight software implementation, subsystem integration & test (I&T), and vehicle I&T. The computer vision engineering team will work closely with related teams, including Sensors, GNC, Avionics, Systems, Flight Software, Mission Operations, and Ground Software. The Computer Vision Engineer will work algorithm design, truth and physics modeling, scene rendering, simulation and analysis for a wide variety of spacecraft and space missions to include but not limited to LEO, MEO, GEO, Reentry, and RPOD (Rendezvous Proximity Operations and Docking). The CV Engineer will help lead successful implementation, validation and CV operations of Anduril’s fleet of space vehicles. This role is directly tied to ongoing, funded programs within Anduril’s Space Business Line. The programs require building and fielding a resilient, software-defined spacecraft systems across numerous mission threads. We work with mission partners and customers to deploy reliable and robust capabilities on operationally-relevant fielding timelines to meet complex challenges across the DOD and IC . WHAT YOU’LL DO - Propose and prototype innovative solutions to solve real world problems, leveraging the latest state-of-the-art techniques in the field - Develop and maintain core libraries and runtime applications - Integrate classical and geometric methods in computer vision with ML methods - Work space vehicle CV software and hardware subsystems for various spacecraft efforts in all orbital regimes and work closely with partners for successful implementation - Develop modern, software-defined approaches to autonomous spacecraft operations with maneuvering capabilities to successfully accomplish mission objectives - Develop appropriate test plans and procedures to validate the CV system during ground checkout, on-orbit commissioning and operations - Collaborate across multiple teams to plan, build, and test complex functionality - Coordinate with end-users, other operators and customers to turn needs into features while balancing user experience with engineering constraints - Support challenging schedules during ground testing, launch windows and on-orbit operations of the spacecraft systems - Design of flight software and firmware, algorithms, and simulation products - Development of space vehicle autonomy tools for dynamic space operations - Test process development and execution - Define automated fault detection and responses - Provide hardware-in-the-loop and monte-carlo simulation capabilities REQUIRED QUALIFICATIONS - MS or PhD in Machine Learning, Robotics or Computer Science, Image Science with emphasis on Computer Vision - BS in Computer Science, Machine Learning, Electrical Engineering, or related field - Advanced professional experience developing and benchmarking ML algorithms on large-scale datasets - High proficiency in C++ development in a Linux environment - Experience in one or more of the following: Objection Detection, Object Tracking, Instance Segmentation, Semantic Segmentation, Semantic Change Detection, Natural Feature Tracking (NFT) - Experience in one or more of the following: Visual Odometry, SLAM, Multi-view Geometry, Structure from Motion, 3D Geometry, Discriminative Correlation Filters, Stereo, Neural 3D Reconstruction, Multi-band sensor processing, RGB-D and LIDAR Sensor Fusion - Ability to quickly understand and navigate complex systems and detailed requirements - Familiarity with terminal guidance, rendezvous proximity operations and docking, orbital mechanics with propulsive spacecraft, and/or spacecraft/missile GNC - Clear communication and organizational skills including documentation and training material - Currently possesses and is able to maintain an active U.S. Top Secret security clearance PREFERRED QUALIFICATIONS - Experience with Matlab, Simulink, Python, Go, C++ and/or Linux systems - A desire to work on critical software and hardware designs in the space domain - Strong preference for candidates possessing computer vision, vision navigation, image processing, feature tracking, SLAM, Open CV, and NFT - Experience testing CV subsystems in laboratory environments that mimic the space environmental constraints - Experience with orbital mechanics and resident space object tracking capabilities - Experience conducting spacecraft operations and satellite command and control with an emphasis on system reliability and uptime - Experience with testing/validation leveraging FlatSats, Hardware-in-the-Loop testbeds and digital spacecraft simulators through nominal and fault scenarios - Experience with computer vision and perception algorithms to support GNC operations - Experience developing 3-DOF simplified and 6-DOF high-fidelity dynamics simulation models used for GNC systems analysis and validation - Exposure to US satellite operations policy and constraints for relevant mission threads in all orbits US Salary Range $191,000 - $253,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
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