Manager, AV Evaluation

General Motors • Sunnyvale, CA

2d • $218,800 - $335,300 • Hybrid

About The Position

General Motors is seeking an Engineering Manager to lead a team developing the software, metrics, and analysis systems used to evaluate autonomous driving performance at scale. The Evaluation team builds and evolves metrics, automated workflows, analysis approaches, scenario libraries, continuous evaluation pipelines, and risk assessment and release-gating components that support autonomous vehicle development and verification. The team partners with Autonomy, Simulation, Systems, and Safety organizations to assess end-to-end AV quality and support validated deployment.

The manager will provide technical direction and people leadership for evaluation platforms used in simulation and on-road testing. They will guide the creation of analysis algorithms and statistical and machine-learning methods, oversee evaluation of machine-learning components, and ensure results are presented through clear dashboards and reports. The role also involves applying emerging AI techniques where appropriate, maintaining strong engineering practices, and translating system-level results into actionable feedback for engineering teams and leadership.

The role is remote for candidates outside the specified hub radius. Employees living within 50 miles of Austin, Detroit, Warren, Milford, or Sunnyvale are expected to report to a hub at least three times per week. Travel is expected to be less than 25%. Relocation benefits are available to candidates who qualify under company policy. The salary range is $218,800–$335,300, with an incentive pay program. Eligible employees may participate in a company vehicle evaluation program, subject to motor vehicle report review and program requirements.

Requirements

• At least 8 years of relevant experience in robotics, autonomous systems software, data analysis, ML evaluation, or autonomy analytics, including substantial experience leading technical teams and delivering complex software systems.
• At least 3 years evaluating dynamic systems using numerical and/or machine-learning 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.
• Strong hands-on familiarity with Pandas, NumPy, SciPy, and data visualization libraries for large-scale analysis and reporting.
• Comfort working with C++ codebases, including reading, debugging, and instrumenting core algorithms.
• Demonstrated technical leadership, including architectural decision-making, influencing cross-team designs, and end-to-end ownership of complex services or platforms.
• Strong cross-functional communication and ability to turn ambiguous evaluation needs into clear technical plans.
• Strong analytical curiosity and a disciplined approach to investigating anomalous data and system discrepancies.
• 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.

Nice To Haves

• Experience in autonomous driving or field robotics, including interpreting simulation and field experiment results.
• Experience evaluating robotics or AV systems using camera, lidar, or radar data and working with large-scale time-series analysis.
• Strong data-visualization intuition and ability to present high-dimensional metrics clearly and reliably to technical and non-technical audiences.
• Familiarity with statistical modeling, experimental design, and hypothesis testing for autonomy or simulation evaluation.
• Proficiency in SQL and experience shaping logging, data schemas, and evaluation pipelines for large-scale autonomy testing and performance monitoring.
• Experience with ROS or similar robotics/IPC frameworks, log pipelines, and experiment databases or evaluation platforms.
• Experience with computational geometry, linear algebra, PyTorch, and machine-learning techniques applied to perception, prediction, planning, or control.
• Background contributing to release gating, risk assessment, and safety-related decisions for autonomy systems.
• Experience using AI-assisted development and analytics tools to improve engineering productivity and evaluation coverage.

Responsibilities

• Lead, coach, and grow engineers building autonomy evaluation platforms, metrics, workflows, dashboards, and analysis tools for simulation and on-road testing.
• Set technical direction for systems that inspect autonomous driving software performance across interfaces and the autonomy stack.
• Drive the design and delivery of algorithms that summarize, aggregate, and cluster metrics from simulation and on-road runs.
• Guide development of statistical and machine-learning methods to quantify performance and identify behavior patterns across scenes and operational domains.
• Oversee evaluation approaches for machine-learning components in perception, prediction, and planning, ensuring they are explainable, scalable, and useful to development and verification teams.
• Ensure delivery of dashboards and interactive reports for trend analysis, drift detection, scenario coverage, and leadership insight.
• Partner with Autonomy, Simulation, Systems, and Safety teams to define requirements, resolve handoff issues, and align evaluation strategy with product and release needs.
• Help apply emerging AI techniques, including VLMs and LLMs where appropriate, to classify autonomy performance and prioritize validation work with human-in-the-loop review.
• Maintain high technical standards through system design, code review, testing, observability, and engineering best practices.
• Translate system-level results into clear feedback for engineering and leadership and drive execution against high-priority evaluation outcomes.
• Build an inclusive, high-performing team through hiring, coaching, feedback, and career development.
• Create clarity around priorities, roadmaps, and dependencies; balance near-term delivery with long-term platform investments.
• Improve execution through clear ownership, design reviews, and effective operating mechanisms.
• Represent the team across partner organizations and influence decisions affecting system-level AV quality.

Benefits

• Incentive pay program with payouts based on company performance, job level, and individual performance.
• Eligible employees may participate in a company vehicle evaluation program and be assigned a General Motors vehicle, subject to program requirements.
• Relocation benefits for candidates who qualify under company policy.
• Benefits supporting employee well-being at work and at home; specific benefits are not detailed in the posting.

What This Job Offers

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