Senior Machine Learning Engineer, End-to-End Planning

Role:

• Develop and maintain high-performance low-latency end-to-end planning algorithms that enable vehicles to safely accurately and reliably navigate highway urban and parking scenarios in production autonomous driving applications.
• Design reusable and optimized behavior and motion planning software architectures for Level 2/3/4 autonomous driving systems.
• Develop and implement advanced machine learning algorithms for data-driven driving behavior and end-to-end planning.
• Lead software development by applying full-stack autonomy expertise to design and deploy algorithms in closed-loop pipelines and real-world vehicle systems.
• Collaborate closely with cross-functional teams to ensure seamless robust implementation and deployment of motion planning products for autonomous driving systems.

Required Qualifications:

• Masters degree or Ph.D. in Computer Science Robotics Machine Learning or a related field.
• Professional experience implementing reliable production systems involving vision-action models vision-language-action (VLA) models generative models such as diffusion or flow-matching models and autonomous driving performance metrics.
• Expert-level knowledge of hands-on experience with Python and PyTorch.
• Solid understanding of state-of-the-art models and approaches in perception prediction and planning for autonomous driving.
• Strong foundation in physics probability and statistics.
• Experience leveraging AI-assisted development tools to improve software engineering efficiency code quality and development workflows.

Preferred Qualifications:

• Experience building and validating real-time autonomous driving or robotics systems including planning and behavior prediction.
• Proficiency in C with strong software design and engineering skills.