Senior Machine Learning Perception Engineer, Fallback Driving System

What you'll do

• Design, train, and evaluate ML models for object detection, segmentation, tracking, and motion prediction using multi-modal sensor data.
• Develop and maintain the secondary perception model to ensure safe vehicle behavior during primary system failures.
• Define success metrics and conduct systematic experiments to improve model performance in edge cases and degraded conditions.
• Analyze large datasets to create selection and labeling strategies for long-tail scenarios.
• Implement optimized training and inference pipelines using techniques like pruning, quantization, and distillation to meet hardware constraints.
• Integrate models into production systems by collaborating with software and infrastructure engineers on deployment and monitoring.
• Translate system requirements from safety and product teams into concrete technical specifications and validation criteria.
• Execute verification and validation strategies including simulation, hardware-in-the-loop, and on-road testing.

What we're looking for

• BS, MS, or PhD in Machine Learning, Robotics, Computer Science, or a related technical field.
• 3–5 years of experience developing ML solutions in perception, prediction, and/or autonomous driving.
• Experience with multi-modal sensor data including camera, lidar, and radar for preprocessing and fusion.
• Expertise in deep learning architectures for object detection, segmentation, tracking, and motion prediction.
• Proficiency in Python and at least one major ML framework such as PyTorch, TensorFlow, or JAX.
• Solid software engineering skills with experience working in C++ within large, collaborative codebases.
• Ability to define ML metrics, design experiments, and systematically improve model performance and robustness.
• Strong communication and cross-functional collaboration skills to work with safety, systems, and product teams.