General Motors
Technical Lead Manager, Sensing Systems Design & Validation
$189K–$291K
Sunnyvale, California, United States of America · Hybrid · Manager · Full-time · Posted Oct 3, 2026
- Experience
- 10 or more years of progressive experience; minimum 1 year leading a small team of engineers.
- Education
- Bachelor’s degree in computer science, computer engineering, electrical engineering, systems engineering, robotics, mathematics, or a related technical field.
About the job
Leads the architecture, development, and validation of sensing-system evaluation capabilities for autonomous driving, including sensor models, simulation, data, tools, and cross-team engineering decisions.
What you'll do
- Set technical direction for sensing evaluation frameworks and integrate sensor models into simulation and evaluation environments.
- Develop statistical methods, synthetic data, metrics, and tools to assess sensor performance and support model training, testing, and architecture decisions.
- Lead cross-functional technical initiatives and manage, coach, and develop a small engineering team.
What they ask for
- Bachelor’s degree in a relevant technical field.
- 10 or more years of progressive experience in a related field, including at least 1 year leading a small team of engineers.
- Strong programming ability in C++ and Python, with production-quality software, testing, debugging, and automation.
- Experience with simulation, evaluation, validation, or data-processing systems in a safety-critical domain; machine learning workflows and a modern framework; and camera, LiDAR, RADAR, or multimodal sensing.
Nice to have
- Master’s degree or Ph.D. in a related technical discipline.
- Experience with synthetic data, sensor degradation, perception models, safety-critical validation, or sensing architectures.
Pay
$189,300 - $290,700 USD base salary
Tools
C++, Python, PyTorch, TensorFlow, Robot Operating System (ROS), Linux, SQL / BigQuery
AI focus
Applies machine learning to sensor and perception evaluation, synthetic and recorded dataset preparation, model training and evaluation, and initial model retraining or pipeline demonstrations.
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