Engineering Manager - Autonomy Evaluation

Job Description

General Motors is a global leader in advanced driverassistance. WithSuper Cruisehands-free technology in more than 500,000 Super Cruise–equipped vehicles on the road and over 700 million hands-free miles driven, GM is proving that automation can be trusted, intuitive, and helpful. GM has the global reach to bringcutting-edgeadvances to everyday drivers atunprecedentedscale. Join us to help deliver the next generation of safe and delightful personal autonomous vehicle experiences.

About the Organization

TheEvaluation teambuilds and evolves the evaluation ecosystem that powers the development and scaling of GM’s autonomous driving technology. We develop metrics, automated workflows, and analysis approaches that enable data-driven decisions across AV development and verification. Partnering with Autonomy, Simulation, Systems, and Safety teams, we act as system-level integrators and arbiters of end-to-end AV quality.

We own large-scale test scenario libraries, continuous evaluation pipelines, and critical risk assessment and release-gating components, treating road testing, data mining, training, and metrics as first-class use cases in a unified analytics framework. By joining this team, you will help shape GM’s core evaluation platforms, turn system-level results into clear feedback for engineering and leadership, and help accelerate validated AV deployment at scale.

We are looking for an Engineering Manager to lead a team building the software, metrics, and analysis systems used to evaluate autonomous driving performance at scale. This leader will combine strong technical judgment with people leadership, cross-functional influence, and execution rigor to help shape GM’s core evaluation platforms and accelerate validated AV deployment.

What You’ll Do (Responsibilities)

Lead, coach, and grow a team of engineers building autonomy evaluation platforms, metrics, workflows, dashboards, and analysis tooling for simulation and on-road testing.

Set technical direction for systems that introspect autonomous driving software performance across interfaces and across the autonomy stack.

Drive the design and delivery of analysis algorithms that summarize, aggregate, and cluster metrics from simulation and on-road runs.

Guide the team in developing new statistical and machine learning methods to quantify performance and identify behavior patterns across scenes and operational domains.

Oversee evaluation approaches for ML components across perception, prediction, and planning, ensuring methods are explainable, scalable, and useful to development and verification teams.

Ensure the team delivers clear dashboards and interactive reports for trend analysis, drift detection, scenario coverage, and leadership insight.

Partner closely with Autonomy, Simulation, Systems, and Safety teams to define requirements, resolve handoff