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

Nominal is hiring a Staff Research Engineer to define how its agents are evaluated and build the path from evaluation to post-trained models. The role focuses on creating hardware-grounded benchmarks and evaluation infrastructure, and leading model improvement when deployment or cost needs justify it.

Responsibilities:

• 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