What You’ll Do (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 human-in-the-loop review where needed.
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.
Your Skills & Abilities (Required Qualifications)
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 Computer Science, Robotics, Mechanical or Aerospace Engineering, Machine Learning, Data Science, or a related field, or equivalent practical experience
What Will Give You a Competitive Edge (Preferred Qualifications)
Experience in autonomous driving or field robotics, including visualizing and interpreting results from simulation and field experiments.
Experience evaluating robotics or AV systems using sensor data (e.g., camera, lidar, radar) and large-scale time-series analysis.
Strong intuition for data visualization and the ability to decompose high-dimensional metrics into clear, trustworthy, and consumable views for technical and non-technical audiences.
Familiarity with statistical modeling, experimental design, and hypothesis testing for autonomy or simulation evaluation; fluency with Pandas, NumPy, SciPy, and visualization tools.
Proficiency in C++ and SQL; experience shaping logging, data schemas, and evaluation pipelines for large-scale autonomy testing and performance monitoring.
Experience working with ROS or similar robotics/IPC frameworks, log pipelines, and large-scale experiment databases or evaluation platforms.
Prior development experience with computational geometry, linear algebra, PyTorch, and ML techniques applied to perception, prediction, planning, or control.
Background in modeling agent interaction and contributing to release gating and safety decisions for autonomy systems.
Experience leveraging AI-assisted development and analytics tools to improve productivity and evaluation coverage.
Hybrid
This role is categorized as hybrid. This means the successful candidate is expected to report to the Sunnyvale or Mountain View office three times per week.
This job may be eligible for relocation benefits if you are interested in relocating to the bay area.
Compensation
The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position, as well as geography of the selected candidate.
The salary range for this role is : $144,700 - $221,400. The actual base compensation within the identified range will vary based on factors relevant to the position.
Bonus Potential
An incentive pay program offers payouts based on company performance, job level, and individual performance.
Benefits
GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays.
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This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}.


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