Staff / Senior Staff Machine Learning Engineer - Collision Avoidance System

In this role, you will:

• Serve as the technical lead and architect for CAS perception, defining how lidar, radar, and camera data become reliable obstacle detection, tracking, and collision-risk estimates under tight latency and compute budgets
• Design, implement, and ship both machine learning and classical perception algorithms (e.g., point-cloud geometry, ground estimation, clustering, state estimation and tracking, learned detection and segmentation, sensor fusion) in production-quality, safety-critical C++
• Own the end-to-end CAS algorithm architecture and multi-release technical roadmap, making clear trade-offs between safety, performance, latency, compute, and ML vs. non-ML approaches
• Partner across CAS perception, behavior and planning, and validation to define interfaces, requirements, metrics, and evaluation strategies that show the system is safe and ready to launch
• Work with sensor, system performance, safety strategy, and main autonomy teams to drive cross-team technical decisions, while mentoring engineers and raising the technical bar across CAS

Qualifications

• BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field, or equivalent experience

• At least 10 years of experience developing perception or related algorithms for robotics, autonomous vehicles, or other real-time systems

• Deep expertise in 3D perception from lidar, radar, and/or camera, with hands-on experience in both machine learning (detection, segmentation, fusion) and classical methods (geometry, state estimation, tracking)

• Strong C++ or Python skills and a track record of shipping efficient, reliable real-time software to production

• Experience leading the architecture and technical roadmap of a complex production system across multiple teams

Bonus Qualifications

• Conference or Journal publications in Machine Learning or Robotics-related venues

• Experience with safety-critical systems or functional safety practices (e.g., ISO 26262, SOTIF), collision avoidance, emergency braking, prediction, or planning systems for autonomous vehicles

• Experience optimizing and deploying models or algorithms on onboard compute (e.g., GPU / CUDA, TensorRT)