GM’s autonomy stack generates far more numerical data than anyone can review manually. The Evaluation Insights team builds tools that turn this data into a single, trustworthy view of performance—accelerating model iteration and improving vehicle safety.
Requirements
- 3+ years applied experience in data analysis, ML evaluation, or autonomy analytics, working with large-scale datasets and statistical methods.
- Proficiency with Pandas, NumPy, SciPy, and plotting/visualization libraries.
Responsibilities
- Design and implement analysis algorithms that summarize, aggregate, and cluster metrics produced by simulations of the autonomy stack
- Build and maintain GM’s primary autonomy evaluation dashboards and reports that provide clear, explainable insights to engineering and leadership, including trend analysis, drift detection, and scenario coverage.
- Leverage vision-language models (VLMs) and large language models (LLMs) to classify autonomy performance, mine critical scenarios, and prioritize validation efforts, integrating human-in-the-loop where appropriate.
- Maintain a high technical standard through architectural design, code reviews, and by following software-engineering best practices.
- Interface with cross-org partners to articulate requirements, resolve handoff issues, and share best practices.
Other
- Bachelor’s or higher degree in Computer Science, Data Science, Mechanical or Aerospace Engineering, or equivalent practical experience.
- A strong understanding of how to visualize quantitative information effectively and transparently. The ability to decompose a multi-dimensional space into something consumable.
- Experience evaluating robotic systems, including sensor data (camera, lidar, radar) and time-series analysis.
- A strong curiosity to question anomalous data and root-cause discrepancies
