Engineering Manager - Autonomy Evaluation
Not Disclosed•Full-TimeOn-site
location_onGranchina, Calle Guayama, 00917, United States
General Motors is a global leader in advanced driver assistance, proving that automation can be trusted, intuitive, and helpful. With Super Cruise hands-free technology in more than 500,000 vehicles and over 700 million hands-free miles driven, GM is bringing cutting-edge advances to everyday drivers at unprecedented scale.
The Evaluation team builds and evolves the ecosystem that powers the development and scaling of GM's autonomous driving technology. We act as system-level integrators and arbiters of end-to-end AV quality, partnering with Autonomy, Simulation, Systems, and Safety teams. We own large-scale test scenario libraries, continuous evaluation pipelines, and critical risk assessment components, treating road testing, data mining, training, and metrics as first-class use cases in a unified analytics framework.
You will lead a team building the software, metrics, and analysis systems used to evaluate autonomous driving performance at scale. This role combines strong technical judgment with people leadership, cross-functional influence, and execution rigor. You will help shape GM's core evaluation platforms, turn system-level results into clear feedback for engineering and leadership, and accelerate validated AV deployment.
In this position, you will drive the design of analysis algorithms that summarize and aggregate metrics from simulation and on-road runs. You will guide the team in developing new statistical and machine learning methods to quantify performance and identify behavior patterns. Your day-to-day involves translating complex system-level results into actionable insights, ensuring evaluation approaches for ML components are explainable and scalable, and leveraging emerging AI techniques to prioritize validation work.
Our vision is a world with Zero Crashes, Zero Emissions, and Zero Congestion. We embrace the responsibility to lead the change that will make our world better, safer, and more equitable for all. We believe we all must make a choice every day to drive meaningful change through our words, deeds, and culture. Every day, we want every employee to feel they belong to one General Motors team.
We are looking for adventure-seekers and imaginative thought leaders to help us transform mobility. Our diverse team brings collective passion for engineering, technology, and design to deliver on our vision.
Applicants in the recruitment process may be required to successfully complete role-related assessments and/or pre-employment screenings prior to beginning employment. For more details on how we hire, please visit our careers site.
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Work model: On-site
Granchina, Calle Guayama, 00917, United States
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.
Skills: Python, Pandas, Numpy, Scipy, C++, SQL, Ros, Pytorch, Machine Learning, Data Analysis.
Education: Bachelor's in Computer Science, Robotics, Engineering, ML, or Data Science, or equivalent practical experience; Master's in Computer Science, Robotics, Engineering, ML, or Data Science, or equivalent practical experience; PhD in Computer Science, Robotics, Engineering, ML, or Data Science, or equivalent practical experience.
