Principal Engineer, Autonomy Evaluation

설명

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.
  

다양성 정보

General Motors는 법적으로 금지된 차별을 배제하는 것은 물론 포용성과 소속감을 진정으로 장려하는 직장이 되기 위해 노력하고 있습니다. 당사는 다양성이 보장되는 환경에서 직원들이 역량을 발휘하고 우리 고객을 위한 더 좋은 제품을 개발할 수 있다고 믿습니다. 따라서 입사에 관심 있는 사람이 있다면 포지션별 주요 업무와 자격을 확인하고 본인이 보유한 기술과 능력에 부합하는 모든 포지션에 적극적으로 지원하기를 장려합니다. 지원자는 채용 과정에서 역할 관련 평가(해당하는 경우) 및/또는 채용 전 스크리닝을 통과해야 합니다.  자세한 정보는 GM 채용 과정 안내를 참고하십시오.

공평한 취업 기회 선언 (미국)

General Motors는 공평한 기회를 제공하는 고용주임을 자부합니다.  자격을 만족하는 지원자는 인종과 피부색, 성별, 성적 지향, 성별 정체성, 국적, 장애, 재향 군인 보호법 적용 여부와 상관없이 채용 후보로서 심사를 받습니다. 

숙소 (미국 및 캐나다)

General Motors는 장애인을 포함한 모든 구직자들에게 취업 기회를 제공합니다. 구직이나 취업 지원에 도움이 되는 합리적인 숙소가 필요한 경우 [email protected]으로 이메일을 보내시거나 800-865-7580으로 전화주십시오. 이메일에, 귀하가 요청하는 특정한 숙소에 대한 설명과 귀하가 지원하는 직무와 채용 요청서 번호를 포함해주세요.