8+ years in ML research or engineering with a significant impact on a shipped model or product.
Expertise in post-training techniques for RL, including debugging reward models and instability issues.
Strong statistical skills for designing reliable experiments and interpreting metrics accurately.
Ability to identify and prioritize impactful research problems while discarding less relevant ones.
Judgment in determining the right approach—whether to measure, train, or adjust tools and context.
Proven track record of setting technical direction and elevating team performance.
Experience with modern AI coding agents and a commitment to continuous improvement.
Responsibilities
Set the research agenda for Hardware Intelligence, defining key problems and success metrics each quarter.
Develop methods for storing and representing hardware context for intelligent reasoning over various data types.
Teach agents to utilize existing engineering tools, enhancing their integration into workflows.
Calibrate simulations with real-world data to improve testing and planning processes.
Create distributed intelligence systems that enable coordination across multiple programs and teams.
Implement edge intelligence solutions with agents and models operating close to hardware environments.
Build reinforcement learning environments and validate them with hardware experts to ensure robustness before training.
Lead post-training processes for models that outperform existing benchmarks, supported by data-driven decisions.
Benefits
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 support ($15,000 lifetime)
Wellness stipend ($100/month)
$500/year hardware sensor stipend
401(k) plan
Employee-friendly equity terms
In-office work required 5 days per week
Full Job Description
What you'll do
Set the research agenda for Hardware Intelligence: pick the problems, place the bets, and define what winning looks like each quarter.
Figure out how to store and represent hardware context, so agents reason over telemetry, test campaigns, requirements, designs, and simulations instead of just retrieving them.
Teach agents to work the tools engineers already use, from CAD to simulation to analysis software, through computer use.
Connect simulation to the physical world: calibrate simulations against measured results, and use simulations to plan and explain tests.
Build distributed intelligence: collections of agents that coordinate across programs, fleets, and teams.
Push intelligence to the edge, with agents and models that run on or near the hardware, on-prem, and air-gapped.
Build RL environments and verifiers over real engineering work (test campaigns, anomaly investigations, design-versus-measured checks), validate them with hardware experts, and find where they break before we train on them.
Lead post-training (SFT, RL, distillation) of open-weight models when a model of our own beats a frontier model with better context, and back every call with data.
Hire and mentor researchers, and publish benchmarks and results worth sharing outside Nominal.
What you'll bring
8+ years in ML research or engineering, including a research direction you owned that changed a shipped model or product.
Deep post-training experience with RL, reward models, or verifiers, and the debugging that comes with them: reward hacking, instability, contamination.
Statistical rigor: you design experiments that don't fool you, and you know when a metric has stopped meaning anything.
Research taste: you've picked problems that mattered and killed the ones that didn't.
The judgment to know when to measure, when to train, and when a better prompt, tool, or context is the answer.
A track record of setting technical direction across a team and raising the bar for the people around you.
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.
Nice to have
You've led post-training or evals at a frontier lab or an AI-native company.
You've published benchmarks, environments, or methods that others use.
You've worked on computer-use agents, multi-agent systems, or knowledge representation for agents.
You've worked on scientific ML, simulation, digital twins, or time-series foundation models.
You've trained or served open-weight models on-device, on-prem, or in air-gapped environments.
You've worked in test, reliability, or verification engineering for physical systems.
Benefits/Perks
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
Compensation
The base pay range for this role is $268,000 - $325,000 per year.
