Senior Engineering Manager, Autonomy Evaluation Applications

GM•Sunnyvale, CA

1d•Hybrid

About The Position

The Senior Engineering Manager, Autonomy Evaluation Applications, leads four teams—Driver Performance, Subsystem Evaluation, Scenario Classification, and Test Insights—through their managers and technical leads. The role owns the strategy, roadmap, execution, and staffing for measuring autonomous-driving quality and turning evaluation results into decisions that guide development and software releases. The work includes identifying simulation tests that do not exercise their intended behaviors and ranking subsystem failures that contribute to declines in driving quality. The manager sets priorities and builds a team whose evaluation outputs are trusted and acted on by partner organizations. They collaborate with principal and staff engineers on evaluation methodology and technical direction, and partner with Software Validation and Safety on test strategy and release decisions. The role also translates evaluation results into clear recommendations for engineering teams and executive leadership, including what changed, the confidence in the results, and recommended next steps.

Requirements

At least 10 years of experience in autonomous systems, robotics, machine-learning evaluation, applied data science, or large-scale data platforms, with a record of turning analysis into decisions and shipped improvements.
At least 5 years of people leadership, including leading managers or technical leads across multiple teams.
Demonstrated ability to hire and develop strong managers and engineers and build teams that perform amid shifting priorities.
Experience owning strategy, roadmap, and execution for a distinct technical area, with measurable outcomes for cross-functional customers.
Hands-on expertise evaluating machine-learning or autonomy systems, including metric design, classifier precision and recall, sampling, experimental design, and statistical confidence; ability to identify when an analysis does not support its conclusion.
Strong stakeholder engagement and ability to build trusted relationships with partner teams and leadership and maintain alignment through changing priorities and disagreement.
Production Python and SQL experience in team environments, and comfort reviewing designs and code.
Experience influencing priorities across organizations without direct authority using trusted data.
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.

Nice To Haves

Experience in autonomous driving or field robotics, including driving-log analysis and results from 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 and ability to turn high-dimensional metrics into clear views for technical and non-technical audiences.

Responsibilities

Own the Eval Applications strategy, roadmap, and quarterly plans in alignment with autonomous-vehicle program milestones and release needs.
Set priorities for what teams build, stop, and deliver, and determine the order of work.
Ensure partner teams understand the meaning and accuracy of metrics and scene classifiers and when to rely on them.
Connect observed driving behavior to ranked problems and fixes; ensure issues from road and simulation testing are sized, routed to the owning team, and tracked to closure.
Enable teams to assess whether software or model changes improve driving, including reliable regression detection for release reviews.
Define and track organization-level measures of evaluation quality and impact, such as classifier precision and recall, time to produce a trusted answer, and the influence of findings on decisions.
Maintain technical quality through design reviews, code review standards, testing, and statistically sound analysis, working with principal and staff engineers on technical direction.
Introduce methods such as VLM- and LLM-based classification and triage with human review, and AI-assisted development where beneficial.
Present clear recommendations to engineering and executive leadership, explaining changes, confidence, and next steps.
Hire, develop, and retain managers, technical leads, engineers, and data scientists, and develop future leaders.
Lead through managers and technical leads by establishing clear ownership, operating rhythms, and accountability.
Conduct performance management fairly and directly.
Adjust team structure and staffing as priorities change while balancing immediate program needs with longer-term investments in methods and tooling.
Resolve cross-team conflicts about priorities, ownership, and contradictory results, and align stakeholders on next steps.
Work with Autonomy Evaluation and AV leadership on long-term evaluation strategy and decisions affecting release confidence.

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
Incentive pay program with payouts based on company performance, job level, and individual performance
Company vehicle evaluation program, subject to motor vehicle report review and program conditions