Technical Lead, Evaluation Infrastructure

Evaluation Infrastructure plays a critical role at Nuro, directly enabling L4 driverless deployment. The team supports two demanding workloads: day-to-day Autonomy Evaluation that powers rapid software iteration, and large-scale Driverless Safety Validation that produces the rigorous evidence required to deploy autonomy on public roads. The Evaluation Infrastructure team builds the metrics framework, evaluation pipelines, introspection tooling, and analysis products that turn raw on-road and simulation logs into actionable insight. Our metrics stack spans both heuristic and ML-based approaches, covering everything from low-level component accuracy to end-to-end behavior quality. The platform empowers autonomy and Systems & Safety teams to run complex evaluations and validations across a wide range of configurations and scales, producing the high-fidelity metrics that drive both short-term iteration and long-term release confidence — in close partnership with Simulation and the broader AI Platform. As the Technical Lead, you will lead the team with deep technical guidance and rigor, setting the technical bar, shortening the time-to-signal for evaluation and the time-to-confidence for validation, so that both autonomy and Systems & Safety teams can iterate fast while deploying software safely.

• You have a degree in B.Sc or M.Sc., plus 4 years of relevant work experience • Domain experience: Strong fluency in distributed systems, large-scale data and ML evaluation pipelines, metrics frameworks (heuristic and/or ML-based), and analytics platforms • Engineering leadership: Experience setting technical vision, roadmap, and prioritization for a team operating at the intersection of autonomy, safety, and data infrastructure; a clear, concise communicator who partners effectively with PMs, engineers, and cross-functional stakeholders across Autonomy, Systems & Safety, and Simulation • Technical excellence: Ability and willingness to deep-dive into implementation; sets the technical bar for metric quality, pipeline rigor, and safety-critical engineering practice across the broader software organization; strong proficiency in Python, C++, or similar languages • AI-native mindset: Daily user of modern AI coding assistants and agentic tools (Claude Code, Cursor, and similar), with strong intuition for where they accelerate engineering work and where they don't; eager to apply LLMs and ML systems to evaluation problems, from automated triage and metric generation to natural-language analysis of fleet behavior; raises the team's productivity, code quality, and signal density through thoughtful AI integration