Staff Research Engineer, Agent Evals & Post-training - UK

United Kingdom·Londonfull-timelead

Software EngineeringSearch Engineer

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Quick Summary

Key Responsibilities

datasets, model-based and human graders, and regression gates in CI. Turn evals into reward signals and training data, and lead post-training (fi

Technical Tools

Software EngineeringSearch Engineer

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 the team behind Nominal's agents, AI-native applications, and MCP, and its forward-leaning AI bets. Our mission is to unlock the bottlenecks of the hardware lifecycle with AI. Our agents reason over physical reality, from high-rate telemetry and test campaigns to designs and simulations, where real test results are the ground truth their work is checked against. We believe opinionated AI, built for the real work of hardware programs, will change how the world engineers.

We're collaborative, iterative, and high-agency, and we're human-centered and customer-focused. We build with the newest AI tools every day, and because those tools keep changing, so do we: we stay curious and keep looking for the better way. Our team spans data science and ML, distributed systems, search, and knowledge systems, and we obsess over how agents can be genuinely useful to the engineers who rely on them.

As a Staff Research Engineer supporting agent evals & post-training, you'll define how Nominal measures its agents, for customers and for ourselves, and build the path from evals to post-trained models when hardware needs them. Evals come first; post-training follows when air-gapped deployment or cost makes it the right investment.

Responsibilities

~1 min read

→Build eval suites for our agents, the MCP tool layer, and our internal company agent, grounded in real hardware tasks.
→Invent new benchmarks for agents working over physical engineering data, where none exist today.
→Benchmark our agents against frontier agents using our MCP, and show where and why ours win.
→Build the eval infrastructure: datasets, model-based and human graders, and regression gates in CI.
→Turn evals into reward signals and training data, and lead post-training (fine-tuning, RL, distillation) when we need models we can run anywhere, including air-gapped environments.
→Set Nominal's strategy for measurement and model improvement, working with hardware experts on what "correct" means.

8+ years in ML engineering or research, including evals or post-training work you led in production.
Statistical rigor: you design evals that don't fool you, and you know when a difference is real.
Deep experience evaluating LLM or agent systems, including model-graded evals and their limits.
Hands-on post-training experience: fine-tuning, RL from feedback, or distillation on real tasks.
The judgment to know when to measure, when to train, and when a better prompt or tool is the answer.
A track record of setting technical direction across a team and raising the bar for the engineers 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

~1 min read

You've built evals or post-training at a frontier lab or an AI-native company.
You've published benchmarks or eval methods that others use.
You've trained or served open-weight models in restricted, on-prem, or air-gapped environments.
You've worked in test, reliability, or verification engineering for physical systems.