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# Staff Research Engineer, Hardware Intelligence

- **Company:** Nominal Inc
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Computer-Aided Design, Artificial Intelligence, Data Analysis, Computer Engineering, Computer Literacy, Cursor, Software Debugging, Machine Learning, Software Engineering, Digital Twin, Claude Code, Multi-Agent Systems, Knowledge Representation
- **Published:** November 13, 2026
- **Apply:** https://www.careerbuilder.com/job-details/staff-research-engineer-hardware-intelligence-austin-tx--9753b128-06f1-428c-b008-94d72d7e5380

## About the Role

Robotics, Computer Engineering, Testing, Artificial Intelligence (AI), Software Engineering, System Operations, Manufacturing, United States Air Force (USAF), Aerospace and Defense, Technical Leadership

## Description

* 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 youll 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 dont fool you, and you know when a metric has stopped meaning anything.
 * Research taste: youve picked problems that mattered and killed the ones that didnt.
 * 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

 * Youve led post-training or evals at a frontier lab or an AI-native company.
 * Youve published benchmarks, environments, or methods that others use.
 * Youve worked on computer-use agents, multi-agent systems, or knowledge representation for agents.
 * Youve worked on scientific ML, simulation, digital twins, or time-series foundation models.
 * Youve trained or served open-weight models on-device, on-prem, or in air-gapped environments.
 * Youve worked in test, reliability, or verification engineering for physical systems.