Staff Research Engineer, Hardware Intelligence

Nominal•Los Angeles, CA

12h•Onsite

About The Position

Nominal is building a Hardware Intelligence Platform to help hardware engineers gain visibility into complex physical systems, make better decisions, and bring products into the real world faster. Its customers work across aerospace, defense, manufacturing, transportation, energy, and robotics. The Hardware Intelligence team is developing agents that are expert at engineering physical systems. The Staff Research Engineer will set the research agenda, decide how real test results can improve context, tools, and models, conduct initial experiments, and grow the researchers who continue the work. The role covers engineering-context representation, computer-use agents, simulation and physical testing, distributed and edge intelligence, reinforcement-learning environments and verifiers, and post-training open-weight models. The engineer will also hire and mentor researchers and publish useful benchmarks and results. The role is in-office five days per week. Applicants must satisfy the stated U.S. Government export-control eligibility requirements or be eligible to obtain the required authorizations.

Requirements

At least 8 years of experience in ML research or engineering.
Ownership of a research direction that changed a shipped model or product.
Deep post-training experience with reinforcement learning, reward models, or verifiers, including debugging issues such as reward hacking, instability, and contamination.
Statistical rigor in experiment design and the ability to recognize when a metric is no longer meaningful.
Research judgment in selecting important problems and ending unproductive efforts.
Judgment about when to measure, train, or use a better prompt, tool, or context.
A record of setting technical direction across a team and raising the bar for colleagues.
Daily use of modern AI coding agents such as Claude Code, Cursor, or Codex, with curiosity and openness to new working methods.

Nice To Haves

Leadership of post-training or evaluations at a frontier lab or AI-native company.
Published benchmarks, environments, or methods used by others.
Experience with computer-use agents, multi-agent systems, or knowledge representation for agents.
Experience in scientific ML, simulation, digital twins, or time-series foundation models.
Experience training or serving open-weight models on-device, on-premises, or in air-gapped environments.
Experience in test, reliability, or verification engineering for physical systems.

Responsibilities

Set the Hardware Intelligence research agenda, select problems, place bets, and define quarterly success criteria.
Determine how to store and represent hardware context so agents can reason over telemetry, test campaigns, requirements, designs, and simulations.
Enable agents to use engineering tools, including CAD, simulation, and analysis software, through computer use.
Calibrate simulations against measured results and use simulations to plan and explain tests.
Build collections of agents that coordinate across programs, fleets, and teams.
Develop agents and models that run on or near hardware, on-premises, and in air-gapped environments.
Build reinforcement-learning environments and verifiers for real engineering work, validate them with hardware experts, and identify failure modes before training.
Lead supervised fine-tuning, reinforcement learning, and distillation of open-weight models when data supports using an in-house model over a frontier model.
Hire and mentor researchers.
Publish benchmarks and research results.