Research Engineer: Hardware AI & RL for Real Systems

Nominal; United States; Remote; USD 130,000 - 210,000; Full time

Benefits offered by this job

100% coverage of medical, dental, and視

Unlimited PTO and sick leave

Free lunch, snacks, and coffee

Professional Development Stipend

In‑office hardware lab with a $250

Annual company retreat

12 weeks parental leave

Fertility and family planning ($15,000

Wellness stipend ($100/month)

$500/year hardware sensor stipend

401(k)

Employee-friendly equity terms

In‑office 5 days per week

Job summary

Nominal is building the Hardware Intelligence Platform to give engineers visibility into physical systems and accelerate real-world deployment. As a ML engineer, you will identify failure modes of agents on real hardware, design evals and data pipelines, and build graders to verify improvements.

You’ll prototype across research bets, run experiments, and help ship high‑impact improvements to our agents and tools used by aerospace, energy, and robotics teams.

Qualifications

• 3+ years in ML engineering or research.
• Hands-on experience improving an LLM or agent system through evals, data, post-training, or harness changes.
• Strong engineering: you write the pipeline, the training script, and the grader yourself.
• Experimental judgment: you design experiments that don't fool you.

Responsibilities

• 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.
• Prototype across our research bets: hardware context for agents, computer use in engineering tools, linking simulation to physical tests.

Skills

ML engineering

Experimentation

Python

Research

System design

Tools

Claude Code

Cursor

Codex

Job description

Nominal is building the Hardware Intelligence Platform to give engineers visibility into physical systems and accelerate real-world deployment. As a ML engineer, you will identify failure modes of agents on real hardware, design evals and data pipelines, and build graders to verify improvements.

You’ll prototype across research bets, run experiments, and help ship high‑impact improvements to our agents and tools used by aerospace, energy, and robotics teams.