Principal Engineer, Autonomy Evaluation

General Motors•Sunnyvale, CA

4d•$250,000 - $384,600

About The Position

The Principal Engineer in Autonomy Evaluation owns how GM evaluates its autonomous driver, from overall driving behavior to individual subsystems. The role develops evaluation methods to assess driving quality, identify subsystem causes of poor behavior, measure the real-world impact of errors, and discover unknown failure modes. The work informs engineering priorities and software release decisions. This senior individual-contributor role sets multi-year technical direction, prototypes and delivers code, and develops Staff and Senior engineers through mentorship and technical guidance. It partners closely with Autonomy, Simulation, Systems, Safety, and Software Validation, and has no direct reports. The role also establishes scientifically rigorous evaluation practices, including sampling, statistical confidence, metric validity, and alignment between simulation and road results. It translates findings into clear recommendations for engineering teams and leadership. The base salary range is $250,000 to $384,600. Eligible employees may participate in a company vehicle evaluation program after successfully completing a motor vehicle report review.

Requirements

10+ years of experience in autonomous systems, robotics, machine-learning evaluation, or applied data science, with a record of turning analysis into decisions and shipped improvements.
Track record of creating evaluation or measurement methods adopted beyond the candidate's own team.
Deep experience designing statistically sound evaluations of dynamic systems, including sampling, confidence intervals, experimental design, hypothesis testing, and precision and recall of classifiers on time-series data.
Strong production Python and SQL skills, including testing and code review.
Ability to start from raw observations, define and size problems, and drive them to resolution using trusted data.
Proven ability to influence without authority, align peers, partner teams, and leadership on technical direction, and secure commitments from organizations the candidate does not manage.
Strong stakeholder engagement and ability to build trusted relationships with partners, understand their evaluation needs, and maintain engagement through ambiguity and disagreement.
Experience growing senior engineers through mentorship, design reviews, and written technical direction.
Ability to turn ambiguous questions and uncertain results into clear recommendations for executive, engineering, and cross-functional audiences.
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 field robotics, including large-scale analysis of driving logs and AV sensor data (camera, lidar, radar) from simulation and on-road testing.
Experience producing evaluation evidence for validation and release decisions, ideally for driver-assistance or automated-driving products.
Experience applying VLMs or LLMs to event classification, triage, or scenario discovery.
Experience with ML model interpretability, such as attribution, ablations, or embedding analysis.
Strong data-visualization instincts and ability to turn high-dimensional metrics into clear, trustworthy views.
Familiarity with C++, especially for reading or contributing to autonomy stack code.

Responsibilities

Set multi-year technical direction for autonomy evaluation, prioritizing methods to develop, invest in, or retire.
Develop methods to assess overall driving behavior, including learned or VLM-based quality judgments, comparison with expert human driving, counterfactual and closed-loop analysis, and discovery of unknown failure modes.
Define evaluation approaches and metrics for perception, prediction, planning, localization, and other subsystems, including learned components, that reflect their effects on driving.
Trace poor driving outcomes to their contributing subsystems or interactions and measure how subsystem errors combine to affect driving.
Determine whether software or model changes improve driving through regression detection and version-to-version comparisons.
Turn observed behavior into measurable and ranked problems; size issues using metrics and scene classifiers, establish problem-space Pareto analyses, and drive fixes with responsible teams.
Ensure scientific rigor through sound sampling, statistical confidence, metric validity, and comparison of simulation and road results; communicate assumptions and limitations and resolve disagreements between evaluation signals.
Present findings as clear recommendations to engineering teams and leadership, including what changed, confidence in the results, and recommended next steps.
Mentor Staff and Senior engineers through research and design reviews and written technical direction.
Prototype and deliver code as part of the evaluation work.

Benefits

Medical insurance
Dental insurance
Vision insurance
Health Savings Account
Flexible Spending Accounts
Retirement savings plan
Sickness and accident benefits
Life insurance
Paid vacation and holidays
Tuition assistance programs
Employee assistance program
GM vehicle discounts
Company vehicle evaluation program, subject to successful motor vehicle report review