Senior Software Engineer, Autonomy Evaluation

Job description

Job responsibilities

• Architect and implement metrics and analyses to introspect autonomous driving software performance at interfaces across the autonomy stack; partner closely with autonomy developers and systems engineers.
• Design and implement analysis algorithms that summarize, aggregate, and cluster metrics produced by simulations and on-road runs of the autonomy stack.
• Propose and develop new statistical and ML methods to quantify performance and identify patterns of system and subsystem behavior across diverse scenes and operational domains.
• Develop and apply methods to introspect the operation of ML components in the autonomy stack, including evaluation of perception , prediction, and planning models.
• Build and maintain autonomy evaluation dashboards and interactive reports that provide clear, explainable insights (e.g., trend analysis, drift detection, scenario coverage) for development, verification, and leadership.
• Leverage vision-language models (VLMs) and large language models (LLMs), where appropriate , to classify autonomy performance, identify critical scenarios, and prioritize validation efforts, integrating…
• Maintain a high technical standard through thoughtful system design, code reviews, testing, observability, and adherence to software-engineering best practices.
• Interface with cross-organizational partners to articulate requirements, resolve handoff issues, and share best practices around evaluation, metrics, and experiment design.

Job requirements

• 5+ years of applied experience with robotics or autonomous systems software (e.g., sensors, perception , prediction, planning, or control), data analysis, ML evaluation, or autonomy analytics.
• 3+ years evaluating dynamic systems using numerical and/or ML approaches, including time-series data, state derivatives, dynamics, and interconnected subsystems.
• Strong proficiency developing Python in production team environments, including testing, performance, and code review.
• Proficiency with Pandas, NumPy, SciPy , and plotting/visualization libraries for large-scale data analysis and reporting.
• Comfort working with C++ codebases, including reading, debugging, and instrumenting core algorithms.
• A strong curiosity to question anomalous data and systematically root-cause discrepancies.
• Demonstrated technical leadership, including driving architectural decisions, influencing cross-team designs, and owning complex features or services end-to-end.
• Bachelor’s, Master’s, or PhD in C omputer Science, Robotics, Mechanical or Aerospace Engineering, Machine Learning, Data Science , or a related field, or equivalent practical experience.