Engineering Manager - Autonomy Subsystem Evaluation

GM • Sunnyvale, CA

1d • Hybrid

About The Position

General Motors is developing the next generation of autonomous driving, with goals including eyes-off highway driving and broader point-to-point autonomy. Autonomy Evaluation uses data from road and simulation miles to identify system limitations, assess the scale and value of problems, guide development priorities, and inform software release decisions.

The Manager of Subsystem Evaluation leads a team of engineers and data scientists and owns the team’s roadmap, execution, and staffing. The team develops metrics for planning, perception, prediction, and localization—including learned components—that assess how subsystem errors affect driving, rather than relying only on component accuracy. It also traces driving failures to the subsystem or interaction responsible. The manager works with autonomy teams, the Driver Performance and Scenario Classification teams, and principal and staff engineers leading evaluation methodology, and reports to the Senior Manager of Eval Applications.

This role is remote, with hybrid attendance required at a GM hub three times per week for employees living within 50 miles of the listed locations. Travel is less than 25%. Relocation benefits are available to candidates who qualify under company policy. The role offers an incentive pay program based on company, job-level, and individual performance. Eligible employees may participate in a company vehicle evaluation program after a motor vehicle report review; participants are generally required to purchase or lease a qualifying GM vehicle every four years, subject to limited exceptions.

Requirements

• At least 8 years of experience in autonomous systems, robotics, ML evaluation, or applied data science, including hands-on evaluation of planning, perception, prediction, or localization.
• At least 3 years managing engineers or data scientists, including hiring and performance management.
• Deep knowledge of autonomy metrics and their limitations, such as planner quality measures, detection and tracking metrics, and trajectory prediction metrics; ability to recognize when a metric improves without the system improving.
• Strong statistical evaluation skills, including sampling, confidence intervals, experimental design, and hypothesis testing on time-series data.
• Disciplined approach to root-causing anomalous data and unexpected system behavior.
• Production Python and SQL experience in team environments, and ability to review designs and code.
• Comfort reading C++ autonomy code to understand subsystem behavior.
• Ability to build trusted relationships with partner teams and influence without authority using credible data.
• Ability to turn ambiguous questions and uncertain results into clear plans and recommendations for engineering teams and leadership.
• Bachelor’s, master’s, or PhD in Computer Science, Robotics, Mechanical or Aerospace Engineering, Machine Learning, Statistics, Data Science, or a related field, or equivalent practical experience.

Nice To Haves

• Experience in autonomous driving or driver assistance, working with simulation and on-road data, including camera, lidar, and radar.
• Understanding of vehicle dynamics and planning and controls system design.
• Experience with causal or counterfactual analysis.
• Experience with ML interpretability, such as attribution, ablations, or embedding analysis.
• Strong data-visualization instincts and ability to turn high-dimensional metrics into clear views for technical and non-technical audiences.
• Experience leading mixed teams of software engineers and data scientists.

Responsibilities

• Set the subsystem evaluation roadmap and quarterly plans in alignment with AV program milestones and release needs; prioritize work and determine what to stop.
• Develop metrics that weight subsystem errors by their effects on downstream decisions and driving, rather than accuracy alone.
• Trace poor driving in simulation and road data to the responsible subsystem or interaction, and measure how subsystem errors combine to affect driving outcomes.
• Provide autonomy teams and release reviews with timely, trustworthy assessments of whether software or model changes improved or harmed driving, including regression detection and version-to-version comparisons.
• Validate metrics against driving outcomes and human judgment; communicate their confidence and limitations, and retire metrics that do not hold up.
• Apply VLMs and LLMs where effective in development, triage, and related processes.
• Communicate clear findings to autonomy teams and leadership, including what changed, confidence in the findings, and what to fix first.
• Hire, develop, and retain engineers and data scientists through clear expectations, regular feedback, and fair performance management.
• Establish clear ownership and operating rhythms; maintain technical quality through design reviews, code review, testing, and statistically sound analysis.
• Balance immediate program requests with longer-term investment in metrics and methods.
• Build trust with autonomy teams, clarify boundaries between component metrics and subsystem evaluation, and resolve disagreements about priorities or conflicting results.

Benefits

• Incentive pay program with payouts based on company performance, job level, and individual performance.
• Company vehicle evaluation program for eligible participants, subject to a motor vehicle report review and program requirements.
• Relocation benefits for candidates who qualify under company policy.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager