Job Description
At Nuro, your base pay is one part of your total compensation package.
Key Responsibilities
- 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.
Requirements
- 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
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