Drimva

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.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 5, 2026 (UTC)