ML Research Engineer

Radical AI is seeking an ML Research Engineer to build systems for understanding, evaluating, and improving a scientific agent's decisions. The role owns evaluation and experimentation infrastructure, develops reproducible datasets and replay systems, analyzes agent trajectories and failure modes, and ships improvements to planning, control, and decision-making in partnership with scientists and platform engineers.

Responsibilities:

• Research and experimentation: work with the Principal Scientist to formulate hypotheses about agent behavior, design controlled experiments, and implement and evaluate improvements to planning, tool use, and decision-making
• Reproducible replay: reconstruct historical agent runs from recorded context so models, prompts, and policies can be compared under consistent conditions, with clear limits on what those comparisons establish
• Versioned evaluation datasets: curated task sets, splits, and ground truth that evolve without invalidating past results
• Evaluation harness and metrics: decision quality, task success, recovery from error, cost, and latency, with honest treatment of noise and small sample sizes
• Trajectory analysis: instrument agent runs, characterize failure modes, and convert recurring failures into regression coverage
• Control policies: prototype, test, and ship the logic that decides when the agent revises an approach, pivots, escalates to a scientist, or terminates
• Experiment infrastructure: data pipelines, artifact and dataset versioning, experiment tracking, and the tooling that lets the team run a clean comparison in minutes rather than days
• Close partnership with our scientists and platform engineers to connect agent behavior to what actually happens in the Workcell