Engineering Manager - Autonomy Evaluation

General Motors•Sunnyvale, CA

1d•$185,100 - $284,100

About The Position

General Motors is a global leader in advanced driver assistance. With Super Cruise hands-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 bring cutting-edge advances to everyday drivers at unprecedented scale. Join us to help deliver the next generation of safe and delightful personal autonomous vehicle experiences. The Evaluation team builds 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.

Requirements

8+ years of relevant experience in robotics, autonomous systems software, data analysis, ML evaluation, or autonomy analytics, including substantial experience leading technical teams and delivering complex software systems.
3+ years evaluating dynamic systems using numerical and/or ML approaches, including time-series data, state derivatives, dynamics, and interconnected subsystems.
Strong proficiency developing Python in production team environments, including testing, performance, and code review.
Strong hands-on familiarity with Pandas, NumPy, SciPy, and data visualization libraries for large-scale analysis and reporting.
Comfort working with C++ codebases, including reading, debugging, and instrumenting core algorithms.
Demonstrated technical leadership, including driving architectural decisions, influencing cross-team designs, and owning complex services or platforms end-to-end.
Strong cross-functional communication and an ability to convert ambiguous evaluation needs into clear technical plans.
Strong analytical curiosity and a disciplined approach to root-causing anomalous data and system discrepancies.
Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Mechanical or Aerospace Engineering, Machine Learning, Data Science, or a related field, or equivalent practical experience.

Nice To Haves

Experience in autonomous driving or field robotics, including interpreting results from simulation and field experiments.
Experience evaluating robotics or AV systems using sensor data such as camera, lidar, and radar, and working with large-scale time-series analysis.
Strong intuition for data visualization and the ability to turn high-dimensional metrics into clear, trustworthy views for technical and non-technical audiences.
Familiarity with statistical modeling, experimental design, and hypothesis testing for autonomy or simulation evaluation.
Proficiency in SQL and experience shaping logging, data schemas, and evaluation pipelines for large-scale autonomy testing and performance monitoring.
Experience with ROS or similar robotics/IPC frameworks, log pipelines, and experiment databases or evaluation platforms.
Prior experience with computational geometry, linear algebra, PyTorch, and ML techniques applied to perception, prediction, planning, or control.
Background contributing to release gating, risk assessment, and safety-related decisions for autonomy systems.
Experience using AI-assisted development and analytics tools to improve engineering productivity and evaluation coverage.

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 issues, and align evaluation strategy with product and release needs.
Help the organization leverage emerging AI techniques, including VLMs and LLMs where appropriate, to classify autonomy performance and prioritize validation work with human-in-the-loop review.
Maintain a high technical bar through strong system design, code review, testing, observability, and engineering best practices.
Translate system-level results into clear feedback for engineering and leadership and drive execution against high-priority evaluation outcomes.
Build a high-performing, inclusive engineering team through hiring, coaching, feedback, and career development.
Create clarity across priorities, roadmaps, and dependencies for a technically complex evaluation domain.
Balance near-term delivery with long-term platform investments in metrics, tooling, and infrastructure.
Raise the quality of team execution through clear ownership, strong design reviews, and healthy operating mechanisms.
Represent the team effectively across partner organizations and influence decisions that affect system-level AV quality.