Engineering Manager - Autonomy Subsystem Evaluation

Job Description

About the Organization

GM has more than 500,000 Super Cruise–equipped vehicles on the road and over 700 million hands-free miles driven. We're now building the next generation of autonomous driving, starting with eyes-off driving on the highway and growing toward point-to-point autonomy. The goal is autonomy 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.

Subsystem Evaluation is one of four teams in Eval Applications, the part of Autonomy Evaluation that turns evaluation into decisions. Each autonomy team measures its own component, usually by accuracy against labels. We answer the question those metrics can't: how much does each subsystem's error actually affect driving? We build metrics for planning, perception, prediction, and localization, including learned components, that reflect their effect on the drive. We also trace driving failures back to the subsystem or interaction that caused them.

The Role

As Manager of Subsystem Evaluation, you'll lead a team of engineers and data scientists and own its roadmap, execution, and staffing. The work is concrete. One example is showing which perception errors change what the car does and which don't. Another is ranking the subsystem failures behind a drop in driving quality, so the right team fixes the right thing first. You'll work with the autonomy teams that use these metrics, with the Driver Performance and Scenario Classification teams, and with the principal and staff engineers who lead evaluation methodology. You'll report to the Senior Manager of Eval Applications.

What You'll Do

• Set the roadmap and quarterly plans for subsystem evaluation, tied to the AV program's milestones and release needs. Decide which subsystems and questions come first, and what to stop.
• Build metrics that weight subsystem errors by their effect on downstream decisions and driving, not by accuracy alone.
• Trace bad driving in simulation and road data back to the subsystem or interaction that caused it, and measure how subsystem errors add up to driving outcomes.
• Give autonomy teams and release reviews a fast, trustworthy answer to whether a software or model change helped or hurt driving, including regression detection and version-to-version comparison.
• Hold the metrics themselves to a high bar. Check them against driving outcomes and human judgment, state their confidence and limits, and retire the ones that don't hold up.
• Apply VLMs and LLMs effectively where applicable in the development/trigaging/etc processes.  
• Turn results into clear findings for autonomy teams and leadership: what changed, how confident we are, and what to fix first.

Leadership Responsibilities

• Hire, develop, and retain engineers and data scientists, with clear expectations, regular feedback, and fair performance management.
• Set clear ownership and steady operating rhythms. Keep the technical bar high through design reviews, code review, testing, and statistically sound analysis.
• Balance near-term program requests against longer-term investments in metrics and methods.
• Build trust with autonomy teams so they act on our metrics. Agree with them on where their component metrics end and ours begin, and resolve disagreements over priorities or conflicting results.

Your Skills & Abilities (Required Qualifications) 

• 8+ years in autonomous systems, robotics, ML evaluation, or applied data science, including hands-on evaluation of planning, perception, prediction, or localization.
• 3+ years managing engineers or data scientists, including hiring and performance management.
• Deep knowledge of autonomy metrics and their limits, such as planner quality measures, detection and tracking metrics, and trajectory prediction metrics. You can tell when a metric improves without the system getting better.
• Strong statistics for evaluation, including sampling, confidence intervals, experimental design, and hypothesis testing on time-series data.
• A disciplined approach to root-causing anomalous data and unexpected system behavior.
• Production Python and SQL experience in team environments, and comfort reviewing designs and code.
• Comfort reading C++ autonomy code to understand what a subsystem actually does.
• Proven ability to build trusted relationships with partner teams and influence them without authority, using data they trust.
• Strong ability to turn ambiguous questions and uncertain results into clear plans and recommendations for engineering 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.

What Will Give You a Competitive Edge (Preferred Qualifications)

• 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 & controls system design.
• Experience with causal or counterfactual analysis.
• Experience with ML interpretability, such as attribution, ablations, or embedding analysis.
• Strong data-visualization instincts, with the 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.

Work Arrangement: This role is categorized as Remote/hybrid. Remote: This role is based remotely but if you live within a 50-mile radius of [Austin, Detroit, Warren, Milford, Sunnyvale, CA ], you are expected to report to that location three times per week, at minimum.

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area. 

• The salary range for this role is ($218,800 - $335,300). The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position. 

• Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance. 

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. Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.

#LI-SA2

This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}. The selected candidate will be required to travel <25% for this role. Relocation benefits are available for candidates who qualify under company policy.

About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

Why Join Us 

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Benefits Overview

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Non-Discrimination and Equal Employment Opportunities (U.S.)

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We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.

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