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Nominal builds software that lets hardware teams test, iterate, and deploy as fast as software teams.
The Role
Research Engineer improving AI agents for hardware engineering through evaluations, grounded training data, graders, post-training, reinforcement learning, and reproducible research infrastructure. The role investigates agent failures on real hardware tasks, develops benchmarks and verification systems, prototypes agent capabilities, evaluates experiments, and ships successful improvements to customer-facing systems.
Summary Generated by Built In
About Nominal
About Hardware Intelligence
💼 What you'll do
🚀 What you'll bring
⚡️ Nice to have
✨ Benefits/Perks
Engineers today are building some of the most ambitious machines humanity has ever seen. They should have AI that’s purpose-built for them.
Nominal is building the Hardware Intelligence Platform that gives engineers increased visibility into complex physical systems, helps them make better decisions, and ultimately gets their products into the real world faster. It brings together the systems, data, intelligence, and applications hardware engineering requires to meet the AI moment. Hardware engineering teams use Nominal to simulate, test, manufacture, and operate the physical systems that keep the world moving, from spacecraft and autonomous vehicles to next-generation energy systems and robotics.
Our customers span aerospace, defense, manufacturing, transportation, energy, and robotics. They include Anduril, Shield AI, Wisk Aero, Pratt Miller Motorsports, Antares, and Shinkei Systems, amongst others, alongside programs within the U.S. Air Force and Navy. Our team draws from SpaceX, Palantir, Anduril, Applied Intuition, Tesla, and other leading companies, united by a common mission: giving hardware engineers the tools to build the future with speed, safety, and confidence. We're a fast-moving team that owns problems end-to-end, works across disciplines, and thrives at the intersection of hardware and software.
Some of the world’s leading technology investors, including Sequoia, Lightspeed, Founders Fund, General Catalyst, Lux Capital, and Insight Partners, are backing Nominal’s ambition to change how machines are built and operated. We’re looking for engineers who want to take on hard problems and help make a new generation of world-changing hardware possible.
We're going after the biggest opportunities to accelerate how the world engineers: agents that are expert at engineering physical systems, not just competent at calling tools. Nobody has a recipe for that yet. Hardware gives us an edge most domains lack: real test results to check agent work against. You'll set the research agenda, decide where to turn that ground truth into better context, better tools, or models of our own, run the first experiments yourself, and grow the researchers who run the next ones.
- Find where our agents fail on real hardware work, from customer traces and from tasks you design with hardware experts.
- Turn those failures into evals, environments, and training data grounded in real test results.
- Build graders and verifiers that check agent work against measured data, and find where they break.
- Fix what you find: change a prompt, tool, or context, fine-tune, run RL, or distill an open-weight model.
- Prototype across our research bets: hardware context for agents, computer use in engineering tools, linking simulation to physical tests, coordinating collections of agents, and intelligence at the edge.
- Build the research infrastructure the loop runs on: eval harnesses, dataset and trace pipelines, and training and experiment tooling that make the next experiment faster to run and easy to reproduce.
- Separate real gains from noise, ship what wins into the agents customers use, and write up what you learn.
- 3+ years in ML engineering or research. Less is fine if your recent work was doing exactly this at a frontier lab or an AI-native company.
- Hands-on experience improving an LLM or agent system through evals, data, post-training, or harness changes, and proving it worked.
- Strong engineering: you write the pipeline, the training script, and the grader yourself.
- Experimental judgment: you design experiments that don't fool you, and you know when a difference is real.
- Comfort picking the next experiment when nobody can tell you what will work.
- You build with modern AI coding agents (Claude Code, Cursor, Codex) every day, and stay curious and open to better ways of working. The tools keep changing, and so do we.
- You've done post-training (SFT, RL from feedback or verifiable rewards, distillation) on real tasks.
- You've built RL environments, synthetic-data pipelines, or benchmarks that other people use.
- You've worked on computer-use agents, multi-agent systems, simulation, or edge deployment.
- You've worked with time-series, sensor, or scientific data, or with models built over it.
- You've been an engineer in hardware, test, controls, or robotics, or worked closely with those teams.
- 🏥 100% coverage of medical, dental, and vision insurance
- 🏖️ Unlimited PTO and sick leave
- 🍽️ Free lunch, snacks, and coffee
- 🚀 Professional Development Stipend
- 🛠️ In-office hardware lab with a $250 project stipend
- ✈️ Annual company retreat
- 👶 12 weeks parental leave
- 🌱 Fertility and family planning ($15,000 lifetime)
- 💪 Wellness stipend ($100/month)
- 🛠️ $500/year hardware sensor stipend
- 💼 401(k)
- 📈 Employee-friendly equity terms
- 🏢 In-office 5 days per week
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.
ITAR Requirements
To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.
CompensationThe base pay range for this role is $230,000 – $270,000 per year.
Skills Required
- 3+ years of experience in machine learning engineering or research; less experience may be accepted for directly relevant frontier lab or AI-native company work.
- Hands-on experience improving an LLM or agent system through evaluations, data, post-training, or harness changes, with demonstrated results.
- Strong engineering skills, including writing pipelines, training scripts, and graders.
- Experimental judgment and ability to design rigorous experiments that distinguish real improvements from noise.
- Ability to independently select and execute experiments in ambiguous research environments.
- Daily use of modern AI coding agents such as Claude Code, Cursor, or Codex.
- Experience with post-training, including supervised fine-tuning, reinforcement learning from feedback or verifiable rewards, or distillation.
- Experience building reinforcement learning environments, synthetic-data pipelines, or reusable benchmarks.
- Experience with computer-use agents, multi-agent systems, simulation, or edge deployment.
- Experience with time-series, sensor, or scientific data, or models built over such data.
- Experience in hardware, testing, controls, robotics, or collaboration with those teams.
Nominal Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Nominal and has not been reviewed or approved by Nominal.
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Healthcare Strength — 100% employer-paid medical, dental, and vision coverage is repeatedly described as covering both employees and dependents, indicating unusually strong healthcare provisioning. Additional mentions of “Platinum” coverage reinforce the sense of a high-tier plan offering.
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Leave & Time Off Breadth — Unlimited PTO and paid holidays/sick time are presented as part of the core package, suggesting broad time-off benefits on paper. Parental leave is also framed as fully paid in several places, reinforcing overall leave breadth.
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Wellbeing & Lifestyle Benefits — Wellness stipends, daily gourmet lunch (and sometimes dinner), and periodic retreats are consistently highlighted as meaningful lifestyle perks. Learning/development and other small stipends further round out a perks-heavy total rewards posture.
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The Company
What We Do
Nominal builds the essential software stack that enables hardware teams to test and iterate as rapidly as software teams. Nominal empowers engineers to continuously monitor, validate, and deploy innovations, transforming how mission-critical hardware is built and operated.
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