Browse AI Jobs — Page 120

Machine Learning Engineer

Negotiable

ABOUT THE ROLE We are building a recursive self-improvement system — a machine learning system that iteratively improves itself through feedback, evaluation, and automated learning loops. You will help build the engineering pipeline that keeps these loops fast, reliable, and trustworthy: the training and evaluation pipelines, the reward and feedback signals, and the safeguards that prevent a self-improving system from silently degrading or gaming its objectives. This is an engineering-first role with deep reinforcement learning requirements. You should be equally comfortable writing robust production ML code and reasoning about reward design, credit assignment, and why feedback-driven systems become unstable. WHAT YOU'LL DO - Build and maintain the training, evaluation, and deployment loops at the core of the self-improvement system, with a strong emphasis on reproducibility and reliability. - Design and implement reward and feedback signals; investigate and mitigate reward hacking, specification gaming, and distribution drift. - Build evaluation harnesses and metrics before models — because a self-improving system is only as safe as its measurement of “better.” - Own data pipelines and automated data flywheels that feed the learning loop. - Debug subtle model-quality regressions and stabilize training and feedback loops that go non-stationary. - Collaborate with research and product to turn methods into robust, shippable systems. WHAT WE'RE LOOKING FOR Must-Haves: Strong MLE fundamentals (non-negotiable) - Excellent Python and clean, well-tested ML training code. - Solid grasp of data pipelines, distributed / large-scale training, and experiment tracking. - The instinct and skill to debug why a model silently got worse — not just why it crashed. Hands-on experience with a feedback or learning loop (at least one). - Built or owned part of a feedback loop — a reward model, an evaluation harness, or the data pipeline for an RLHF/RLAIF or active-learning system. - Ran a retraining or continual-learning pipeline where a model consumed its own predictions or production data (e.g. ranking, recommendations, fraud, spam). - Fine-tuned LLMs with human or AI feedback, or built agentic evaluation harnesses. Reinforcement learning foundations and curiosity. - Working knowledge of reward modeling, on-policy vs. off-policy tradeoffs, and credit assignment (does not need to be a research-level RL expert). - Has seen — or can reason clearly about — feedback-system failure modes: reward hacking, specification gaming, feedback loops amplifying errors. - Comfortable evaluating non-stationary systems (systems whose behavior and data distribution change over time). Systems and evaluation instinct. - Builds the eval before the model; treats measurement as a first-class deliverable. - Has shipped an ML system into production and kept it healthy over time. STRONGEST SIGNAL Bonus, not required: The ideal candidate has built or shipped a full system that improved from its own outputs or feedback end to end. This is rare at this level, so treat it as a standout differentiator rather than a filter. Examples: - RLHF / RLAIF pipelines - Self-play systems - Active-learning loops - Automated data flywheels - Agentic evaluation harnesses Nice to Have: - PhD or MS in RL / ML paired with real production experience (either the science or the engineering half alone is fine if the other is strong). - Experience at a lab or company doing RLHF, agents, or large-scale ML infrastructure. - Familiarity with LLM fine-tuning, evaluation frameworks, or agent orchestration. WHY JOIN HIPPOCRATIC AI Reinvent healthcare with AI that puts safety first. We’re building the world’s first healthcare‑only, safety‑focused LLM — a breakthrough platform designed to transform patient outcomes at a global scale. This is category creation. Work with the people shaping the future. Hippocratic AI was co‑founded by CEO Munjal Shah and a team of physicians, hospital leaders, AI pioneers, and researchers from institutions like El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft, and NVIDIA. Backed by the world’s leading healthcare and AI investors. We recently raised a $126M Series C at a $3.5B valuation, led by Avenir Growth, bringing total funding to $404M with participation from CapitalG, General Catalyst, a16z, Kleiner Perkins, Premji Invest, UHS, Cincinnati Children’s, WellSpan Health, John Doerr, Rick Klausner, and others. Build alongside the best in healthcare and AI. Join experts who’ve spent their careers improving care, advancing science, and building world‑changing technologies — ensuring our platform is powerful, trusted, and truly transformative. EQUAL OPPORTUNITY Hippocratic AI is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, sex, age, disability, sexual orientation, gender identity or expression, genetic information, military or veteran status, or any other characteristic protected by applicable law. We are committed to building a team that reflects the patients we serve. We actively encourage applications from candidates of all backgrounds. If you require accommodations during the hiring process, please contact people@hippocraticai.com. Please be aware of recruitment scams impersonating Hippocratic AI. All recruiting communication will come from @hippocraticai.com http://hippocraticai.com email addresses. We will never request payment or sensitive personal information during the hiring process.

👤 HumanFull-timeMid

By Hippocratic AISep 23, 2026

Research Scientist Intern (PhD) - Model Team - Lon...

Negotiable

Negotiable

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 Science Teams We are looking for a Research Scientist to join the Multi-Embodiment Generalist Agent (MEGA) team within Wayve Science as a founding member. MEGA is building foundation models for general-purpose robots: models that learn from large-scale video, language, and robot-interaction data, then generalize across tasks and embodiments, including mobile manipulators, dual-arm platforms, and humanoids. Our aim is to build agents that can perceive, reason about, and act reliably in the physical world. You will help define and build the foundation-model learning stack for robotics: novel model architectures, pre-training objectives, post-training methods, and scalable data and training systems. The work combines frontier ML research with a direct route to real-world evaluation on a growing fleet of robots. Your work may span vision-language-action models, world and action models, video and multimodal models, imitation learning, reinforcement learning, and self-supervised learning. You will work with large-scale video and robotics datasets and distributed training infrastructure to develop increasingly capable, robust, and general robot policies. You will collaborate with research scientists, ML engineers, roboticists, and hardware teams to turn promising ideas into large-scale experiments, strong research contributions, and compelling robot demonstrations. 🧠 Your day-to-day - Design and run experiments on model architectures, learning objectives and data strategies for robot foundation models. - Build scalable pre-training and post-training methods using web video, egocentric video and robot-interaction data. - Curate, filter, mix and evaluate large robotics datasets, including egocentric, UMI and teleoperated data. - Develop and use distributed training pipelines for large multimodal models and datasets. - Partner with ML engineers, roboticists and hardware teams to turn research progress into measurable real-world robot performance. - Communicate findings through rigorous internal reviews, publications and robot demonstrations. 🧩 What you’ll be working on - Research and develop model architectures, learning objectives, and data strategies for robot foundation models. - Develop scalable self-supervised and generative pre-training methods using web video, egocentric video, and robot-interaction data. - Develop post-training approaches, including supervised fine-tuning, imitation learning, reinforcement learning, and related methods, to improve real-world robot capabilities. - Curate, filter, and evaluate large-scale robotics datasets, including egocentric, UMI, and teleoperated data. - Build and use distributed training pipelines for large models and large multimodal datasets. - Work closely with robotics and hardware teams to connect model progress to measurable real-world performance. - Communicate research clearly internally and, where appropriate, through publications and Wayve’s scientific presence. 🙌 You should apply if - Experience in machine learning, with a focus on multimodal foundation models and data for foundation models. - Experience with scalable training, such as multi-node training, large datasets and/or large model training. - Strong research track record, including publications in top-tier venues such as ICRA, CoRL, CVPR, NeurIPS, ICML or ICLR. - Strong engineering skills and hands-on experience with modern machine learning frameworks. - Ability to design and run rigorous experiments while collaborating closely with engineering and robotics teams. - Experience translating research ideas into working systems, experiments or deployed capabilities. - Strong communication skills and the ability to share research clearly across teams. 🌱 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. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility. How we work 💻- Locations & Flexible Working: Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives. 🔍 The Interview Process: Our process is clear and respectful of your time: - Initial call / recruiter screen (30 mins) - Competency Interviews (Pytorch debugging and hiring manager interview; 1.5 hours total) - Deep-dive technical interviews (Systems & domain-specific interviews; 3 hours total) - Final interview: Mission & values alignment (45 mins). We’ll always explain the format and work around your availability. What’s in it for you (Location dependant): 💰 Salaries benchmarked against the market annually 📈 Meaningful equity, sharing in the ownership and long term success of Wayve ✈️ Relocation support and visa sponsorship where applicable ✅ Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs 📚 Learning and development budgets with support for training, conferences and growth 🩺 Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more 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.

👤 HumanFull-timeMid

By WayveOct 2, 2026

Staff Research Engineer, Multi-Agent Scaling

Negotiable

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: Large teams of agents are starting to solve problems no single agent can, from rewriting major codebases to formalizing landmark mathematics. Our team studies how these teams scale: what happens to performance, cost and coordination as the number of agents, the compute budget and the length of the task grow, and what has to change to keep getting returns from that scale. We build the platform and evaluations Anthropic uses to run and measure large agent teams, and other research teams build on them. This role lives at the boundary between research and engineering. It is a generalist role on a small team: you'll design and run large experiments, build the systems they run on, and get to the bottom of surprising results. We often need to go from a vague question to a running experiment quickly. Responsibilities: - Design, run and interpret large-scale experiments on agent teams, reasoning rigorously about what the data does and doesn't show - Investigate how performance and efficiency change as team size, compute and task horizon grow, and find the bottlenecks that limit them - Build and scale the systems that run very large agent teams reliably, and debug the failures that only appear at scale - Design evaluations for long-horizon problems, and keep their results trustworthy - Build the tooling and metrics that let researchers see what a large agent team is doing and why - Partner with research teams across Anthropic so they can run their own experiments on the platform, and communicate findings clearly You may be a good fit if you: - Have significant software engineering, ML or research engineering experience - Have owned something substantial end to end, such as a large system, an evaluation or benchmark, an agent product, or a research project - Genuinely enjoy both research and engineering work - Think quantitatively about complex systems, and think twice before trusting a number - Can work from a vague question rather than a spec - Are results-oriented, with a bias towards flexibility and impact - Have clear written and verbal communication - Care about the societal impacts of your work Strong candidates may also have: - Experience building or operating large-scale distributed systems, such as schedulers, sandboxed code execution, or inference and RL infrastructure - Built evaluations, benchmarks or harnesses for LLMs or agents - Experience building complex agentic systems that use LLMs - Experience with scaling laws or other large-scale empirical research - A background in operations research, statistics, economics, physics, quantitative finance, or another field that models and optimizes complex systems Strong candidates need not have: - Formal certifications or education credentials - Academic research experience or publication history - Prior experience with multi-agent systems or reinforcement learning Representative projects: - Measure how performance scales with the number of agents on a hard problem, and explain where the curve bends and why - Prepare our largest-ever agent run: find what breaks as team size and task length grow together, and fix it before launch - Work out how to allocate a fixed compute budget across a team of agents to solve a problem fastest - Build tooling that turns the activity of a large agent team into something a researcher can read in minutes - Design a novel eval that distinguishes real gains in teamwork from artifacts of the evaluation setup The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 - $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

👤 HumanFull-timeExpert / Staff+

By AnthropicOct 2, 2026

2027 Summer Intern, PhD, Research, World Action Mo...

Negotiable

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship! You will: - Research, implement, and evaluate world action models for end-to-end autonomous driving - Collaborate with research and engineering teams to improve the Waymo World Model - Conduct cutting-edge research and potentially communicate research findings to the wider academic community via technical reports and/or publication You have: - Currently pursuing a Ph.D. in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a related field - Strong publication record in top-tier AI/ML or robotics conferences - Hands-on experience training and evaluating deep learning models for autonomous driving or robotics applications - Strong Python coding skills with expertise in modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow) - Deep understanding of modern neural network architectures and learning paradigms We prefer: - Experience with diffusion / flow matching models - Experience with world action models in autonomous driving or robotics applications - Familiarity with autonomous driving datasets - Experience with open-source foundation models Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in. The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly PhD Pay $85 - $85 USD

👤 HumanContractEntry

By WaymoOct 2, 2026

Applied Scientist/Machine Learning Engineer, Gaia

Negotiable

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 Simulation Teams Generative Simulation is the team advancing our end-to-end autonomous driving research, incubating and investing in new ideas that can become game-changing technological advances for Wayve. As a Machine Learning Engineer in the team, you will play a key role in developing next-generation world models and planners, such as GAIA, that can simulate complex, diverse, and temporally consistent driving environments to power faster training, broader testing, and scalable deployment, even in areas and scenarios we have never driven in before. As we push toward the next generation of GAIA, efficiency and interactivity are a major focus: models must run thousands of roll-outs per second, support closed-loop agent interaction, and fit within practical compute budgets. You will work at the intersection of machine learning research, multi-modal modelling, and real-world deployment, tackling questions such as how we can deploy autonomous vehicles in a new geography without collecting any real-world data, and whether synthetically generated environments can fully replace physical testing and data collection. 🧠 Your day-to-day - Setting technical direction: Set technical direction and designing key components of the system. - Deep work / coding: You spend a focused block implementing and debugging new modeling capabilities. - Cross-team collaboration, brainstorming and pairing: you brainstorm ideas with your team mates or pair to solve a difficult problem - Mentorship / async review: You review a teammate’s pull request or experimental design and outcomes and provide feedback - Sync-ups: Share findings with the rest of your team leading to a discussion on designing the next experiments. You align on shared priorities for the week and blockers. - Paper reading/ reading groups: Read one of the latest papers and summarise them for yourself or the team to present in the next reading group if interesting 🧩 What you’ll be working on - Invent next-generation, efficient generative world-models (diffusion, transformer or hybrid) that deliver real-time roll-outs and controllable scene editing. - Architect interactive world models where agents (or humans) can step the model, enabling reinforcement learning, planning and safety evaluation loops. - Optimise end-to-end performance – from latent compression to context pruning – your aim is to reduce inference latency by orders of magnitude. - Define robust metrics for long-horizon coherence, physics fidelity and planner integration; run ablations and scaling studies to understand trade-offs. - Ship impact: integrate your models into closed-loop training and evaluation, and measure the sim-to-real gap against on-road driving-model results. - Mentor and influence: guide junior researchers, shape technical road-maps, publish at top venues and represent Wayve in the community. - Challenge assumptions and drive innovation: propose bold ideas, conduct ablation studies, and question conventional approaches to training and evaluation. 🙌 You should apply if - 4+ years of experience in ML research/engineering with a focus on generative video, world models. - Deep knowledge in diffusion & latent-video models; track record of improving sampling efficiency or model throughput. - Experience working with high-dimensional temporal or spatial-temporal data (e.g., video, multi-sensor fusion). - Strong Python and PyTorch engineering fundamentals, and experience building research-grade production tools. - Strong publication record or contributions to open-source ML tooling. - Ability to work collaboratively in a fast-paced, innovative, interdisciplinary team environment. 🌱 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. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility. How we work 💻- Locations & Flexible Working: Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives. 🔍 The Interview Process: Our process is clear and respectful of your time: - Initial call / recruiter screen (30 mins) - Competency Interviews (Programming/Pytorch debugging and hiring manager interview; 2 hours total) - Deep-dive technical interviews (Systems & domain-specific interviews; 3 hours total) - Final interview: Mission & values alignment (45 mins). We’ll always explain the format and work around your availability. What’s in it for you (Location dependant): 💰 Salaries benchmarked against the market annually 📈 Meaningful equity, sharing in the ownership and long term success of Wayve ✈️ Relocation support and visa sponsorship where applicable ✅ Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs 📚 Learning and development budgets with support for training, conferences and growth 🩺 Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more 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.

👤 HumanFull-timeMid

By WayveOct 2, 2026

AI Research Engineer

Negotiable

About Graphcore At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence. Job Summary As a research engineer at Graphcore, you will contribute to the advancement of AI research, investigating new ideas that push the limits on important AI/ML problems. Specialised hardware has been the key driver of the progress of AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to advancing this exciting field. We are therefore looking for individuals who combine strong machine learning experience with practical engineering skills to deliver impactful AI research. We are seeking AI researchers with strong software engineering experience, particularly in lower-level programming and performance optimisation for hardware efficiency. Our research spans a broad range of topics, including efficient training and inference, world models, life sciences, reinforcement learning, and beyond. You will work closely with researchers to generate ideas and translate them into scalable implementations, contributing to publications and projects that help to steer the future of AI hardware. The Team Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world. We pride ourselves on being a supportive and collaborative team, where we organise around our individual research interests to solve problems together in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications, including for sequence- and graph-based data. We’re based across London, Cambridge and Bristol, with projects and discussions that involve all our locations. Perhaps the best way to get an idea of what we’re all about is to read one of our papers or an article on our blog . If you’re excited to work at the cutting edge of AI supported by new hardware and want to develop your skills in this area, we’d love to hear from you! Responsibilities and Duties - Generate AI/ML ideas, design experiments, implement them & evaluate results. - Prepare, submit & present your work to AI conferences and workshops. - Provide technical insight to internal teams by designing experiments and delivering clear, actionable reports. - Collaborate with researchers, silicon and software engineers at Graphcore to help define, build and test Graphcore’s next generation of AI hardware. About you: Essential: - Master’s, PhD or equivalent experience in a technical discipline (e.g., Maths, Statistics, Computer Science, Physics, Chemistry). - Python programming in a modern deep learning framework, e.g. PyTorch or JAX. - Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling. - Capable of designing, executing and reporting from ML experiments. - Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton. - Practical familiarity with hardware capabilities for deep learning – threads, caches, vector & matrix engines, data dependencies, bus widths and throttling. - Practical familiarity with software stacks for deep learning – compilation, kernel fusion, XLA/ATen ops, streams, and asynchronous execution Desirable: - Mathematics skills to support the above: calculus, probability theory and linear algebra. - Experience submitting papers to international scientific conferences or workshops. Benefits In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments. Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

👤 HumanFull-timeMid

By GraphcoreJul 14, 2026

Senior Research Engineer - Inference ML

Negotiable

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. About The Role As a Senior Research Engineer on the Inference ML team at Cerebras Systems, you will adapt today's most advanced language and vision models to run efficiently on our flagship Cerebras architecture. You'll work alongside ML researchers and engineers to design, prototype, validate, and optimize models, gaining end-to-end exposure to cutting-edge inference research on the world's fastest AI accelerator. You will focus on pushing the frontier of speculative decoding, large-model pruning and compression, sparse attention, and sparsity-driven techniques to deliver low-latency, high-throughput inference at scale. Responsibilities - Design, implement, and optimize state-of-the-art transformer architectures for NLP and computer vision on Cerebras hardware. - Research and prototype novel inference algorithms and model architectures that exploit the unique capabilities of Cerebras hardware, with emphasis on speculative decoding, pruning/compression, sparse attention, and sparsity. - Train models to convergence, perform hyperparameter sweeps, and analyze results to inform next steps. - Bring up new models on the Cerebras system, validate functional correctness, and troubleshoot any integration issues. - Profile and optimize model code using Cerebras tools to maximize throughput and minimize latency. - Develop diagnostic tooling or scripts to surface performance bottlenecks and guide optimization strategies for inference workloads. - Collaborate across teams, including software, hardware, and product, to drive projects from inception through delivery. Minimum Qualifications - One of the following education and experience combinations: - Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or a related technical field AND 7+ years of ML software development experience, OR - Master’s degree in Computer Science or related technical field AND 4+ years of software development experience, OR - PhD in Computer Science or related technical field with 2+ years of relevant research or industry experience, OR - Equivalent practical experience. - 4+ years of experience testing, maintaining, or launching software products, including 2+ years of experience with software design and architecture. - 3+ years of experience in software development focused on machine learning (e.g., deep learning, large language models, or computer vision). - Strong programming skills in Python and/or C++. - Experience with Generative AI and Machine Learning systems. - Evidence of research impact in machine learning, such as publications at top conferences (NeurIPS, ICLR, ICML, ACL, EMNLP, MLSys) or comparable contributions to widely used open-source projects or high-quality preprints. Preferred Qualifications - Master’s degree or PhD in Computer Science, Computer Engineering, or a related technical field. - Experience independently driving complex ML or inference projects from prototype to production-quality implementations. - Hands-on experience with relevant ML frameworks such as PyTorch, Transformers, vLLM, or SGLang. - Experience with large language models, mixture-of-experts models, multimodal learning, or AI agents. - Experience with speculative decoding, neural network pruning and compression, sparse attention, quantization, sparsity, post-training techniques, and inference-focused evaluations. - Familiarity with large-scale model training and deployment, including performance and cost trade-offs in production systems. - Triton/CUDA experience is a big plus. Required Skills & Attributes - Proficiency with at least one major ML framework (PyTorch, Transformers, vLLM, or SGLang). - Deep understanding of transformer-based models in language and/or vision domains, with demonstrated experience implementing and optimizing them. - Proven ability to translate research ideas into robust code: implementing new model variants, training strategies, and evaluation workflows end-to-end. - Strong foundation in performance optimization on specialized hardware (e.g., GPUs, TPUs, or HPC interconnects). - Deep understanding of modern ML architectures and strong intuition for optimizing their performance, particularly for inference workloads using sparse attention, pruning/compression, and speculative decoding. - Track record of owning problems end-to-end and autonomously acquiring whatever knowledge is needed to deliver results. - Self-directed mindset with a demonstrated ability to identify and tackle the most impactful problems. - Collaborative approach with humility, eagerness to help colleagues, and commitment to team success. - Genuine passion for AI and a drive to push the limits of inference performance. - Hybrid role in Toronto, ON, CA or Sunnyvale, CA, USA. Why Join Cerebras People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras: 1. Build a breakthrough AI platform beyond the constraints of the GPU. 2. Publish and open source their cutting-edge AI research. 3. Work on one of the fastest AI supercomputers in the world. 4. Enjoy job stability with startup vitality. 5. Our simple, non-corporate work culture that respects individual beliefs. Find out more about what it's like to work at Cerebras here https://www.cerebras.ai/join-us! Apply today and become part of the forefront of groundbreaking advancements in AI! Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them. This website or its third-party tools process personal data. For more details, click here https://www.cerebras.net/privacy/ to review our CCPA disclosure notice.

👤 HumanFull-timeSenior

By Cerebras SystemsOct 2, 2026

PhD Research Intern, Quantum Simulation and AI - 2...

Negotiable

NVIDIA Research is seeking leading researchers in the area of simulation and AI for quantum. You should have a strong background of research excellence, software systems building experience, and interest in advancing the state-of-the-art in quantum computing and applications of GPUs to the quantum computing ecosystem. Specific areas of research interest include but are not limited to bespoke quantum representations, tensor network methods, quantum surrogate modeling, quantum inverse design, and automated proving for quantum systems. What you’ll be doing: Research and develop creative and innovative algorithms and software systems for the quantum computing ecosystem. Collaborate with research and product teams in the areas of machine learning, programming systems, and quantum computing. Publish and present your original research, speak at conferences and events. Collaborate with external quantum computing researchers. Run large-scale experiments on large clusters of GPUs to advance the state-of-the-art in quantum computing simulation. What we need to see: Currently pursuing a Ph.D. in Quantum Physics or related fields 1+ years of relevant research experience in quantum computing with a strong research record, well-referenced publications and/or patents. Strong basis in linear algebra and expertise in math for quantum computing and tensor algebraic methods. Experience with Python and, ideally, CUDA Experience with commonly used machine learning frameworks (PyTorch, Tensorflow, JAX). Strong communication and teamwork skills. NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.” We're looking to grow our company, and build our teams with the smartest people in the world. Join us at the forefront of technological advancement. Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The expected hourly rate for this position is 38 USD – 94 USD. You will also be eligible for Intern benefits . Applications for this job will be accepted at least until November 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

👤 HumanContractMid

By NVIDIAOct 30, 2026

Principal Networking AI Systems Architect

Negotiable

We are seeking a highly skilled Principal Networking AI System Architect to join as a key contributor to the Applied Networking AI group. In this role you will scope and lead AI based solutions for networking technologies and drive their integration across teams.You’ll lead a portfolio and roadmap of projects that encompass agentic-AI for data-center management, predictive-resiliency, optimization and more. By collaborating closely with subject-matter-experts (SMEs), applied-researchers, product-managers, architects, data-engineers and other stakeholders you will push the envelope forward in using cutting-edge technologies and data-driven insights to improve NVIDIA's products. What you'll be doing: Build a shared roadmap and vision for AI based data-center management solutions spanning LLM intelligence for troubleshooting, predictive-resiliency and AIOPS, black-box optimization and performance tuning. Work closely with engineering and reliability teams to scope and define workflows utilizing and benefitting from AI/ML. Drive the integration of AI capabilities into system architecture and engineering workflows. Identify system-level opportunities for failure management, automated troubleshooting, performance improvement, and resource optimization. Translate system behavior, dependencies, data, and operational constraints into formulated research problems. What we need to see: Ph.D in electrical engineering, machine-learning, computer-science or another relevant field. 10+ years of deep technical experience in high-performance network architecture, data center networking, or distributed systems design. Mastery of high-speed interconnect protocols including InfiniBand and/or advanced Ethernet architectures. Thorough experience driving high-impact projects centered on modern AI/ML such as LLMs/agents, deep-learning, black-box optimization or another relevant field. Deep knowledge of AI/ML and networking-hardware/system-architecture. Excellent ability to convey and communicate data-based insights to stakeholders and management. Experience demonstrating an excellent track of collaboration with hands-on teams. Ways to stand out from the crowd: Demonstrated track record of architecting and deploying multi-thousand-node GPU clusters for hyperscale cloud environments. Deep knowledge of NVIDIA networking technologies, including BlueField DPUs, Quantum InfiniBand switches, and Spectrum Ethernet platforms. Expertise in in-network computing, telemetry, adaptive routing, and telemetry-driven network optimization. High energy and a positive, proactive and curious approach.

👤 HumanFull-timeExpert / Staff+

By NVIDIASep 3, 2026

Senior/Staff Software Engineer, AI Agent Infrastru...

Negotiable

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 Frontier models are fungible. Any team can rent the same intelligence we can, and the model we build on today will be replaced within a month. What is not fungible is the infrastructure that decides whether an autonomous system's output can be trusted — evaluation, verification, and the discipline to gate on evidence instead of impressions. Nuro has spent a decade building exactly that discipline for a robot that drives on public roads, and this team turns it inward: we build the platform that lets AI agents operate autonomously inside Nuro's own engineering organization, under the same standard of proof we apply to the vehicle. Our mandate is to amplify the output of every engineer and researcher at Nuro by 100x. Not a better IDE, not a faster build — a change in what a single person can attempt. That number is a target, not a claim, and reaching it depends on one thing above all: autonomous work has to be trustworthy enough to run unattended. So our central ambition is to build the most rigorous closed-loop evaluation system for AI work anywhere. Leverage follows from trust, and trust follows from measurement. We operate as a startup inside a company that has already shipped a hard thing. Small team, no established playbook, direct access to compute and to the systems we are automating. You will work directly with engineering leadership and the CEO, and the decisions you make will be yours to make rather than yours to implement. About the Role We already operate a substantial agent system in production — a fleet of agents with an extensive library of skills and plugins, integrated into the tools our engineers use daily. This role is about what it takes to make that system trustworthy, autonomous, and an order of magnitude more capable. Three things sit at the center of it. Closed-loop evaluation. Our ambition is to build the most rigorous evaluation system for AI work anywhere — closed-loop, meaning every agent action produces a measurable outcome that feeds back into whether that agent is trusted to act again. Acceptance, revert, and override rates per workflow. Statistical honesty about whether a difference is real. Regression detection that fires before a human notices. Everything else on this team depends on this being right, and almost nobody has built it well. Agent platform. The runtime that makes autonomous agents safe to run against real systems: orchestration, sandboxing and isolation, tool and skill frameworks, memory, identity and permissioning, and the gateway and observability layer underneath. Agents that touch production code and production infrastructure need containment and auditability before they need capability. Autoresearch infrastructure. The automation of the research loop itself: agents that read the current state of a model and its metrics, form a hypothesis, launch an experiment, evaluate the result honestly, and either propose a change or discard the idea and move on. At Nuro that loop runs against the training pipelines behind the driving model — real experiments, real compute budgets, real metrics that determine whether a behavior ships. The hard parts are trusting the measurement, surviving experiments that take days, spending finite research compute wisely, and producing proposals a skeptical researcher can audit and reject. Alongside this, the team builds agent-powered tooling across the engineering lifecycle — code generation, review, debugging, test and CI failure attribution, knowledge retrieval, triage. There is also appetite on this team for post-training our own models where an internal workload justifies it, and the engineer in this role would be central to that work. About the Work What You Might Own in Your First Two Quarters - Build the closed-loop measurement layer that tells us, per workflow, whether agent output is accepted, reverted, or overridden — and use it to decide where autonomy expands and where it gets pulled back. - Take the autoresearch loop from assisted to unattended for a bounded class of experiments, including the eval and confidence machinery required to run it without a human in the loop. - Design the isolation and permissioning model that lets agents act on production repositories and infrastructure with an auditable record of what they did and why. About You - 5+ years of software engineering experience (or 4+ with a Master's) in computer science, engineering, or equivalent practical experience. Staff-level candidates should bring correspondingly deeper scope and ownership. - Deep, current taste in LLM research. You understand how a model is trained from scratch — data, tokenization, architecture, pretraining dynamics, the full post-training stack of supervised fine-tuning, preference optimization, and RL — and you can reason about what a training decision does to model behavior. You follow the literature because you want to, not because it is on a roadmap. - You know what happens under the hood at inference. Attention and KV-cache behavior, batching and scheduling, quantization, speculative decoding, prefix caching, context handling, and how each trades off latency, throughput, and cost. - You have built and operated LLM-based agent systems in production — tool use, orchestration, sandboxing, retrieval, memory — and you know where they break. - Strong backend and distributed systems background at scale: cloud infrastructure, service design, storage, queuing, and the judgment to build things that stay up. - Strong programming skills in Python. - You are opinionated about evaluation. You have argued with someone about whether a benchmark measured anything real, and you were right. - You work end-to-end and do not need the problem pre-decomposed. This role has more surface than a specification. - Hands-on post-training or fine-tuning experience — SFT, preference optimization, RL, distillation — including the evaluation work required to know whether it helped. - Experience with ML training or research infrastructure: experiment orchestration, evaluation pipelines, hyperparameter search, data pipelines. - Experience running inference serving, cost, or capacity at meaningful scale. - Familiarity with agent architecture patterns: planning, reflection, long-horizon memory, multi-agent coordination. - Experience with open tool-integration protocols, plugin or skill frameworks, and model-routing or gateway layers. - Background in developer experience or platform engineering, observability, or security isolation. - Go, C++, or Rust experience in addition to Python. 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 $352,290 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package. At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics.

👤 HumanFull-timeExpert / Staff+

By NuroJul 31, 2026

Staff Engineer, Computer Vision (C++) (R5196)

Negotiable

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: This role is for a Computer Vision C++ Engineer to support the development of real-time perception capabilities for Shield AI's autonomous systems. The Computer Vision Engineer will be responsible for designing, developing, and integrating advanced computer vision algorithms into high-performance C++ software pipelines. The role will focus on building custom perception capabilities from the ground up, with an emphasis on real-time performance, reliability, and deployment in edge compute environments. This position is required to increase the team's capacity and technical depth in real-time computer vision, image processing, and applied machine learning. The successful candidate will bring strong C++ software engineering skills, experience developing high-performance or real-time systems, and a deep understanding of computer vision fundamentals. They should be capable of translating research concepts into deployable software and comfortable working on bespoke algorithms rather than relying only on existing libraries. Experience with areas such as camera calibration, multi-view geometry, geospatial registration, 3D reconstruction, image rectification, sensor alignment, coordinate transformations, or target localization would be highly valuable. The role will also support the development of geometry-based computer vision pipelines, including applications such as camera modelling, intrinsic and extrinsic calibration, image-to-world projection, geolocation from imagery, visual registration, and multi-sensor spatial alignment. Familiarity with photogrammetry, epipolar geometry, bundle adjustment, terrain or map-based registration, and deployment of geometry algorithms into real-time C++ systems would be beneficial. Candidates with a Master's or PhD in Computer Science, Engineering, Robotics, Computer Vision, Geomatics, Photogrammetry, or a related field would be well aligned with the technical needs of the team, though strong applied industry experience is also highly relevant. Required qualifications: Degree in Computer Vision, Computer Science, Engineering, or related technical field. Deep proficiency in C++, with proven experience developing software in high-performance, real-time environments. Demonstrated experience designing and implementing custom computer vision algorithms from scratch, rather than exclusively applying existing libraries. Solid foundational understanding of computer vision, image processing, and machine learning principles. Strong software engineering fundamentals, including data structures, algorithms, performance optimization, and version control. Proven ability to own technical work from ambiguous requirements through algorithm design, implementation, integration, and delivery. Ability to collaborate effectively with multidisciplinary cross-functional teams, including AI experts, robotics engineers, and optical engineers, to deliver reliable edge solutions. Preferred qualifications: We do not expect candidates to have all of the following. Experience in any of these areas will help you stand out. Master’s or PhD in Computer Science, Engineering, or a related field. Experience with object detection, target tracking, 3D reconstruction, SLAM, camera calibration, or behaviour analysis. Background in automated video surveillance or other related real-time vision systems. Familiarity with industry-standard vision toolkits, such as OpenCV. Exposure to deep learning methodologies for image classification, recognition, or sequence modelling. Practical experience integrating deep learning models into broader computer vision systems for real-time deployment scenarios. Previous experience in the Defence sector, autonomous unmanned systems, or similar mission-critical domains.

👤 HumanFull-timeExpert / Staff+

By Shield AIJul 15, 2026

Senior Engineer, Computer Vision (C++) (R5195)

Negotiable

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: This role is for a Computer Vision C++ Engineer to support the development of real-time perception capabilities for Shield AI's autonomous systems. The Computer Vision Engineer will be responsible for designing, developing, and integrating advanced computer vision algorithms into high-performance C++ software pipelines. The role will focus on building custom perception capabilities from the ground up, with an emphasis on real-time performance, reliability, and deployment in edge compute environments. This position is required to increase the team's capacity and technical depth in real-time computer vision, image processing, and applied machine learning. The successful candidate will bring strong C++ software engineering skills, experience developing high-performance or real-time systems, and a deep understanding of computer vision fundamentals. They should be capable of translating research concepts into deployable software and comfortable working on bespoke algorithms rather than relying only on existing libraries. Experience with areas such as object detection, image classification, target recognition, semantic segmentation, feature extraction, object tracking, video analytics, dataset curation, model evaluation, or deployment of trained perception models would be highly valuable. The role will also support the development and integration of learned perception models into real-time computer vision pipelines. This may include adapting detection, classification, recognition, or segmentation models for operational imagery; improving model performance across varied lighting, viewpoint, background, and environmental conditions; evaluating false positives and false negatives; and working with representative datasets to improve robustness. Familiarity with combining learned models with classical computer vision techniques, optimizing inference for edge deployment, and integrating model outputs into C++ perception systems would be beneficial. Candidates with a Master's or PhD in Computer Science, Engineering, Robotics, Computer Vision, Machine Learning, or a related field would be well aligned with the technical needs of the team, though strong applied industry experience is also highly relevant. Required qualifications: Degree in Computer Vision, Computer Science, Engineering, or related technical field. Deep proficiency in C++, with proven experience developing software in high-performance, real-time environments. Demonstrated experience designing and implementing custom computer vision algorithms from scratch, rather than exclusively applying existing libraries. Solid foundational understanding of computer vision, image processing, and machine learning principles. Strong software engineering fundamentals, including data structures, algorithms, performance optimization, and version control. Proven ability to own technical work from ambiguous requirements through algorithm design, implementation, integration, and delivery. Ability to collaborate effectively with multidisciplinary cross-functional teams, including AI experts, robotics engineers, and optical engineers, to deliver reliable edge solutions. Preferred qualifications: We do not expect candidates to have all of the following. Experience in any of these areas will help you stand out. Master’s or PhD in Computer Science, Engineering, or a related field. Experience with object detection, target tracking, 3D reconstruction, SLAM, camera calibration, or behaviour analysis. Background in automated video surveillance or other related real-time vision systems. Familiarity with industry-standard vision toolkits, such as OpenCV. Exposure to deep learning methodologies for image classification, recognition, or sequence modelling. Practical experience integrating deep learning models into broader computer vision systems for real-time deployment scenarios. Previous experience in the Defence sector, autonomous unmanned systems, or similar mission-critical domains.

👤 HumanFull-timeSenior

By Shield AIJul 15, 2026

Senior Staff Engineer, Computer Vision (C++) (R519...

Negotiable

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: This role is for a Computer Vision C++ Engineer to support the development of real-time perception capabilities for Shield AI's autonomous systems. The Computer Vision Engineer will be responsible for designing, developing, and integrating advanced computer vision algorithms into high-performance C++ software pipelines. The role will focus on building custom perception capabilities from the ground up, with an emphasis on real-time performance, reliability, and deployment in edge compute environments. This position is required to increase the team's capacity and technical depth in real-time computer vision, image processing, and applied machine learning. The successful candidate will bring strong C++ software engineering skills, experience developing high-performance or real-time systems, and a deep understanding of computer vision fundamentals. They should be capable of translating research concepts into deployable software and comfortable working on bespoke algorithms rather than relying only on existing libraries. Experience with areas such as visual SLAM, visual odometry, target tracking, feature detection and matching, 3D reconstruction, state estimation, sensor fusion, or real-time mapping would be highly valuable. The role will also support the development of tracking and spatial perception pipelines for autonomous systems, including applications such as visual localization, motion estimation, scene reconstruction, landmark tracking, map building, and perception-assisted navigation. Familiarity with SLAM frameworks, probabilistic estimation, optimization, bundle adjustment, feature-based and direct methods, and deployment of real-time perception algorithms into C++ edge environments would be beneficial. Candidates with a Master's or PhD in Computer Science, Engineering, Robotics, Computer Vision, Autonomous Systems, or a related field would be well aligned with the technical needs of the team, though strong applied industry experience is also highly relevant. Required qualifications: Degree in Computer Vision, Computer Science, Engineering, or related technical field. Deep proficiency in C++, with proven experience developing software in high-performance, real-time environments. Demonstrated experience designing and implementing custom computer vision algorithms from scratch, rather than exclusively applying existing libraries. Solid foundational understanding of computer vision, image processing, and machine learning principles. Strong software engineering fundamentals, including data structures, algorithms, performance optimization, and version control. Proven ability to own technical work from ambiguous requirements through algorithm design, implementation, integration, and delivery. Ability to collaborate effectively with multidisciplinary cross-functional teams, including AI experts, robotics engineers, and optical engineers, to deliver reliable edge solutions. Preferred qualifications: We do not expect candidates to have all of the following. Experience in any of these areas will help you stand out. Master’s or PhD in Computer Science, Engineering, or a related field. Experience with object detection, target tracking, 3D reconstruction, SLAM, camera calibration, or behaviour analysis. Background in automated video surveillance or other related real-time vision systems. Familiarity with industry-standard vision toolkits, such as OpenCV. Exposure to deep learning methodologies for image classification, recognition, or sequence modelling. Practical experience integrating deep learning models into broader computer vision systems for real-time deployment scenarios. Previous experience in the Defence sector, autonomous unmanned systems, or similar mission-critical domains.

👤 HumanFull-timeExpert / Staff+

By Shield AIJul 15, 2026

Computer Vision Engineer (C++) (R4633)

Negotiable

Negotiable

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! About this Role We are hiring a senior engineer for our Photonics Characterization team at Lightmatter. Lightmatter is redefining computing by building photonic systems that use light to enable high-bandwidth, energy-efficient AI computing. In this role, you will lead advanced characterization, bring-up, validation, and root-cause analysis of semiconductor lasers, tunable laser sources, DFB lasers, external-cavity lasers, laser diodes, and semiconductor optical amplifiers. This role requires strong hands-on lab expertise, deep understanding of laser physics and semiconductor device behavior, and the ability to connect measured device performance to design, packaging, system, and reliability implications. You will own complex optical and electro-optical test setups, develop robust measurement methodologies, automate characterization flows, analyze large datasets, and translate results into clear engineering conclusions. You will also work closely with laser design, photonics, packaging, systems, reliability, and software teams to guide device development from early engineering samples through production-oriented validation. Responsibilities - Lead hands-on characterization of semiconductor lasers, tunable laser sources, DFB lasers, DBR/ECL lasers, laser diodes, and semiconductor optical amplifiers. - Measure and analyze key laser and SOA performance metrics, including output power, wavelength, optical spectrum, SMSR, RIN, linewidth, gain, saturation power, ASE, noise figure, tuning behavior, optical stability, wavelength stability, and thermal sensitivity. - Develop advanced LIV, L-I-V-T, wavelength tuning, power stability, RIN, SMSR, linewidth, and long-term drift measurement methodologies. - Characterize devices across current, voltage, temperature, optical loading, polarization, optical feedback, aging, and operating conditions. - Analyze SOA gain, gain saturation, wavelength-dependent gain, polarization-dependent gain, ASE noise, carrier dynamics, thermal behavior, and nonlinear effects. - Build, validate, and maintain automated optical and RF measurement setups using Python, PyVISA, LabVIEW, MATLAB, or similar tools. - Use modeling and design-analysis software to support characterization planning and data interpretation, including tools for laser physics, optical gain, rate-equation analysis, thermal modeling, optical spectrum analysis, and system-level performance estimation. - Develop scripts and software workflows for instrument control, automated data collection, calibration, data processing, visualization, statistical analysis, and automated reporting. - Debug complex optical, electrical, RF, and thermal measurement issues, including optical coupling, polarization sensitivity, back-reflection, RF noise, grounding, calibration error, thermal drift, and instrument limitations. - Define measurement best practices, calibration procedures, data quality checks, and test documentation standards. - Translate characterization results into actionable feedback for laser design, photonics integration, packaging, system architecture, and reliability teams. - Support device bring-up from early engineering samples through mature validation and production-readiness phases. - Mentor junior engineers and help establish high-quality characterization practices across the team. - Prepare clear technical reports, design reviews, data summaries, and recommendations for cross-functional engineering teams. Qualifications - PhD or Master’s degree in Electrical Engineering, Physics, Applied Physics, Photonics, Optics, or a related technical field. - 5+ years of hands-on experience in semiconductor laser, SOA, photonics, or optical device characterization. - Strong understanding of semiconductor laser physics, optical gain, carrier dynamics, rate equations, thermal effects, feedback sensitivity, linewidth, RIN, and optical noise mechanisms. - Deep hands-on experience with optical and electro-optical lab measurements. - Strong familiarity with laser characterization metrics such as threshold current, slope efficiency, wall-plug efficiency, LIV curves, SMSR, RIN, linewidth, wavelength tuning, optical spectrum, power stability, and wavelength stability. - Strong understanding of semiconductor optical amplifiers, including gain, saturation behavior, ASE, noise, carrier dynamics, polarization dependence, wavelength dependence, and thermal effects. - Experience operating optical and RF instruments, including laser current drivers, TEC controllers, OSAs, optical power meters, photodetectors, RF spectrum analyzers, oscilloscopes, bias tees, optical filters, polarization controllers, optical switches, and fiber-coupled components. - Strong Python programming experience for instrument automation, data analysis, plotting, statistical analysis, and automated reporting. - Experience using software tools for device design support, modeling, simulation, or data-driven analysis. - Strong analytical skills and ability to debug open-ended device, setup, and measurement problems independently. - Ability to communicate complex technical results clearly and drive conclusions across design, characterization, packaging, and systems teams. Preferred Qualifications - PhD in Electrical Engineering, Physics, Applied Physics, Photonics, Optics, or a related field. - Prior experience characterizing DFB lasers, DBR lasers, tunable lasers, ECLs, laser diodes, or SOAs for integrated photonics or optical communication systems. - Experience with advanced laser-noise measurements, including RIN, linewidth, phase noise, frequency noise, optical feedback sensitivity, and relaxation oscillation analysis. - Familiarity with laser and SOA modeling, including rate-equation modeling, gain modeling, thermal modeling, optical feedback analysis, and wavelength tuning behavior. - Experience using commercial or internal simulation/design tools for photonic device analysis, laser design support, or system-level optical performance modeling. - Experience with III-V semiconductor materials, heterogeneous integration, laser packaging, fiber coupling, optical isolators, and thermal control. - Experience with high-speed optical or RF measurements involving fast photodetectors, RF spectrum analyzers, oscilloscopes, LNAs, bias tees, and calibration/de-embedding. - Experience developing automated test platforms for device characterization and validation. - Experience analyzing large characterization datasets and creating automated dashboards or reports. - Experience supporting reliability, qualification, or production-oriented validation of semiconductor lasers or optical amplifiers. - Demonstrated ability to lead technical work independently, define test strategy, mentor junior engineers, and influence cross-functional engineering decisions. 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. $210,000 - $265,000 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.

👤 HumanFull-timeSenior

By LightmatterJul 2, 2026

16,591 results

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