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.
Evaluation Applications is the part of Autonomy Evaluation that turns evaluation into decisions. It has four teams. Driver Performance measures overall driving quality. Subsystem Evaluation measures how subsystems such as perception, prediction, planning, and localization affect driving. Scenario Classification builds the scene classifiers that tag what happened in every drive and simulation run. Test Insights turns results into findings for engineering and release reviews.
The RoleAs Senior Manager of Eval Applications, you will lead these four teams through their managers and tech leads, and own the roadmap, execution, and staffing for how we measure driving quality and turn it into decisions. The work is concrete: using scene classifiers to find simulation tests that don't exercise the behavior they were written to check, or ranking the subsystem failures behind a drop in driving quality so the right team fixes the right thing first. You'll set priorities, build the team, and make sure partner teams trust and act on what we produce. You'll work with the org's principal and staff engineers, who lead evaluation methodology, and with the Software Validation and Safety teams, who use our results in test strategy and release decisions.
What You'll Do - Own the strategy, roadmap, and quarterly plans for Eval Applications, tied to the AV program's product milestones and release needs. Decide what the teams build, what they stop, and in what order.
- Make evaluation results trusted and used. Partner teams in Autonomy, Simulation, Systems, Safety, and Software Validation should know what each metric and scene classifier means, how accurate it is, and when to rely on it.
- Run the loop from observed driving behavior to ranked problems to fixes, so issues found in road and simulation testing get sized, routed to the owning team, and tracked to closure.
- Make sure teams can tell quickly and reliably whether a software or model change improved driving, including regression detection that release reviews can depend on.
- Set and track org-level measures of evaluation quality and impact, such as classifier precision and recall, time from a new question to a trusted answer, and how often our findings change a decision.
- Keep the technical bar high through design reviews, code review standards, testing, and statistically sound analysis, and work with principal and staff engineers on technical direction.
- Bring in new methods where they pay off, including VLM- and LLM-based classification and triage with human review, and AI-assisted development across the team.
- Turn results into clear recommendations for engineering and executive leadership: what changed, how confident we are, and what to do next.
Leadership Responsibilities - Hire, develop, and retain managers, tech leads, engineers, and data scientists, and grow the next layer of leaders.
- Lead through managers and tech leads with clear ownership, steady operating rhythms, and real accountability. Run performance management fairly and directly.
- Shape team structure and staffing as priorities change, balancing near-term program needs against longer-term investments in methods and tooling.
- Resolve cross-team conflicts over priorities, ownership, and contradictory results, and align stakeholders on what to do next.
- Work with Autonomy Evaluation and AV leadership on long-term evaluation strategy and the decisions that affect release confidence.
Your Skills & Abilities (Required Qualifications) - 10+ years of experience in autonomous systems, robotics, ML evaluation, applied data science, or large-scale data platforms, with a track record of turning analysis into decisions and shipped improvements.
- 5+ years of people leadership, including leading managers or tech leads across multiple teams.
- A track record of hiring and developing strong managers and engineers, and building teams that do well when priorities shift.
- Ownership of the strategy, roadmap, and execution for a distinct technical area, with measurable outcomes for cross-functional customers.
- Hands-on depth in evaluating ML or autonomy systems, including metric design, classifier precision and recall, sampling, experimental design, and statistical confidence. You can review an analysis and tell when its conclusion doesn't hold.
- Strong stakeholder engagement. You build trusted relationships with partner teams and leadership, and keep them aligned through changing priorities and disagreement.
- Production Python and SQL experience in team environments, and comfort reviewing designs and code.
- A record of influencing priorities across organizations without direct authority, using data that others trust.
- Strong ability to turn ambiguous questions and uncertain results into clear plans and 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 (Preferred Qualifications) - Experience in autonomous driving or field robotics, including driving-log analysis and results from both simulation and on-road testing.
- Experience leading teams that produced evaluation evidence for validation and release decisions, ideally for driver-assistance or automated-driving products.
- Experience evaluating at least one autonomy subsystem (perception, prediction, planning, or localization) and relating it to overall driving behavior.
- Experience applying VLMs or LLMs to event classification, triage, or scenario discovery.
- Experience leading mixed teams of software engineers and data scientists.
- Strong data-visualization instincts, with the ability to turn high-dimensional metrics into clear views for technical and non-technical audiences.
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 New York, Colorado, California, or Washington.
- The salary range for this role: $296,300 - $453,900. 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.
- 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.
Work Arrangement: This role is categorized as Remote/
hybrid . Remote:This role is basedremotely but if you live within a 50-mile radius of [Austin, Detroit, Warren,Milford,
Sunnyvale ], you are expected to report to that location three timesperweek, at minimum.
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.
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