Principal Engineer, Autonomy Evaluation

General Motors; United States; Remote; USD 250,000 - 385,000; Full time

Benefits offered by this job

Health and wellbeing benefits

GM vehicle discounts

Tuition assistance programs

Job summary

General Motors Company seeks a Principal Engineer in Autonomy Evaluation to own how we evaluate autonomous driving systems, from overall behavior to subsystems. You will prototype and land code, mentor Staff and Senior engineers, and collaborate with Autonomy, Simulation, Safety, and Software Validation teams to deliver trustworthy release decisions.

You will design statistically sound evaluations, quantify driving quality, and translate observations into actionable recommendations for

Qualifications

• 10+ years of experience in autonomous systems, robotics, ML evaluation, or applied data science, with a track record of turning analysis into decisions and shipped improvements.
• A track record of creating new evaluation or measurement methods that were adopted beyond your own team.
• Deep experience designing statistically sound evaluations of dynamic systems, including sampling, confidence intervals, experimental design, hypothesis testing, and the precision and recall of classifiers on time-series data.

Responsibilities

• Set the multi-year technical direction for evaluation. Decide which questions matter most, which methods to invest in, and which to retire.
• Invent new ways to judge overall driving behavior, such as learned or VLM-based judges of driving quality, comparison with how expert humans drive, counterfactual and closed-loop analysis, and discovery of unknown failure modes in the long tail.
• Define how we evaluate subsystems such as perception, prediction, planning, and localization, including learned components, with metrics that reflect each subsystem's real effect on driving rather than accuracy alone.
• Trace bad driving back to the subsystem or interaction that caused it, and measure how subsystem errors add up to driving outcomes.
• Define how we tell whether a software or model change actually improves driving, including regression detection and version-to-version comparison, so engineering teams and release reviews get trustworthy answers quickly.
• Turn observed behavior into measured, ranked problems. Size issues with metrics and scene classifiers, build a Pareto of the problem space, and drive fixes with the teams that own them.
• Own the scientific rigor of evaluation: sampling, statistical confidence, metric validity, and agreement between simulation and road results. Make every result's assumptions and limits explicit, and resolve the hardest disagreements between signals, such as simulation versus road or a subsystem metric versus a driving metric.
• Turn results into clear recommendations for engineering and leadership: what changed, how confident we are, and what to do next.
• Mentor Staff and Senior engineers through research and design reviews and written technical direction.

Skills

Autonomous systems

ML evaluation

Python

SQL

C++

Statistical design

Stakeholder engagement

Mentorship

Leadership without authority

Education

Bachelor's degree in Computer Science / Robotics / EE

Master's degree or PhD in related field

Tools

Linux

Git

Jupyter

Job description

About the Organization

Join the team building GM's next generation of autonomous driving, starting with eyes-off driving on the highway and growing toward point-to-point autonomy. The goal is autonomous capability that is safe, reliable, comfortable, and scalable across GM's vehicle portfolio. Autonomy Evaluation helps the AV program figure out where the driver still falls short. Every mile our vehicles drive, on the road or in simulation, carries that signal. We turn it into answers: what is limiting the system, how big each problem is, and what fixing it is worth. Those answers steer what GM's autonomy teams build next and inform software release decisions. We partner closely with Autonomy, Simulation, Systems, Safety, and Software Validation.

The Role

As a Principal Engineer in Autonomy Evaluation, you will own how we evaluate the autonomous driver, from overall driving behavior down to individual subsystems. You'll take on questions like: How do we measure whether the car drives well, not just safely? When a behavior goes wrong, which subsystem caused it, and how much does that error matter on the road? How do we find the failures we don't know to look for? You'll turn the answers into methods other teams adopt, priorities other organizations commit to, and evidence that release decisions rely on. You'll prototype and land code yourself, and you'll grow the Staff and Senior engineers who carry the work forward. You are a senior individual contributor with no direct reports.

What You'll Do

• Set the multi-year technical direction for evaluation. Decide which questions matter most, which methods to invest in, and which to retire.
• Invent new ways to judge overall driving behavior, such as learned or VLM-based judges of driving quality, comparison with how expert humans drive, counterfactual and closed-loop analysis, and discovery of unknown failure modes in the long tail.
• Define how we evaluate subsystems such as perception, prediction, planning, and localization, including learned components, with metrics that reflect each subsystem's real effect on driving rather than accuracy alone.
• Trace bad driving back to the subsystem or interaction that caused it, and measure how subsystem errors add up to driving outcomes.
• Define how we tell whether a software or model change actually improves driving, including regression detection and version-to-version comparison, so engineering teams and release reviews get trustworthy answers quickly.
• Turn observed behavior into measured, ranked problems. Size issues with metrics and scene classifiers, build a Pareto of the problem space, and drive fixes with the teams that own them.
• Own the scientific rigor of evaluation: sampling, statistical confidence, metric validity, and agreement between simulation and road results. Make every result's assumptions and limits explicit, and resolve the hardest disagreements between signals, such as simulation versus road or a subsystem metric versus a driving metric.
• Turn results into clear recommendations for engineering and leadership: what changed, how confident we are, and what to do next.
• Mentor Staff and Senior engineers through research and design reviews and written technical direction.

Your Skills & Abilities

• 10+ years of experience in autonomous systems, robotics, ML evaluation, or applied data science, with a track record of turning analysis into decisions and shipped improvements.
• A track record of creating new evaluation or measurement methods that were adopted beyond your own team.
• Deep experience designing statistically sound evaluations of dynamic systems, including sampling, confidence intervals, experimental design, hypothesis testing, and the precision and recall of classifiers on time-series data.
• Strong production Python and SQL skills, including testing and code review.
• A record of starting from raw observations, defining and sizing the problem, and driving it to resolution with data that others trust.
• Proven ability to influence without authority: aligning peers, partner teams, and leadership on technical direction, and getting commitments from organizations you don't manage.
• Strong stakeholder engagement. You build trusted relationships with partners such as Autonomy, Simulation, Safety, and Software Validation, understand what they need from evaluation, and keep them engaged through ambiguity and disagreement.
• Experience growing senior engineers through mentorship, design reviews, and written technical direction.
• Strong 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.

What Will Give You a Competitive Edge

• Experience in autonomous driving or field robotics, including large-scale analysis of driving logs and AV sensor data (camera, lidar, radar) from both 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, with the ability to turn high-dimensional metrics into clear, trustworthy views.
• Familiarity with C++, especially for reading or contributing to autonomy stack code.

Compensation

• The base salary range for this role is $250,000 to $384,600. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.

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, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

Company Vehicle

• Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate.