Senior/Staff Machine Learning Engineer (Motion Planning)

• Develop state-of-the-art machine learning models for autonomous vehicle planning using rich map, perception, routing, and contextual sensor data
• Design and implement model architectures for trajectory generation, behavior planning, and decision making that balance accuracy, robustness, interpretability, and runtime efficiency
• Own the end-to-end machine learning lifecycle, including data curation, feature engineering, experimentation, training, evaluation, deployment, monitoring, and continuous improvement
• Design rigorous offline evaluation methodologies, validation pipelines, and metrics to measure planning quality, safety, robustness, and generalization
• Analyze model behavior, investigate failure cases, and improve performance through systematic error analysis and targeted experimentation
• Collaborate closely with runtime, perception, prediction, mapping, and systems teams to deploy scalable machine learning solutions into production
• Design validation strategies and rule-based guardrails to ensure generated trajectories are feasible, safe, and compliant with traffic rules
• Stay current with advances in machine learning, robotics, and autonomous driving, translating research innovations into production systems
• Ensure technical work complies with the company’s Quality Management System (QMS), customer requirements, regulatory standards, and internal engineering processes

Benefits

• Unlimited PTO: Unlimited paid time off on top of company holidays to recharge and enjoy as needed
• Flexible working: Flexibile work arrangements and technology to help manage life’s demands at the office and remote
• Health benefits: Tiered options in medical, dental, and vision insurance to fit your unique needs
• Future building: Equity, 401k and FSA options for all eligible employees to help build for your future
• Professional development: Company-sponsored professional development opportunities such as conferences, training, and workshops
• Catered lunches: Daily catered lunches at both of our Santa Clara and Fremont offices

• 4+ years of experience developing machine learning systems for robotics, autonomous driving, or other real-time decision-making systems
• Strong software engineering skills with experience developing and maintaining production-quality software
• Strong ownership mindset with the ability to drive problems from investigation through implementation, validation, and deployment
• Experience designing datasets, experiments, validation methodologies, and metric-driven model evaluation
• Experience designing validation methodologies, automated testing, and monitoring to ensure correctness, safety, and production reliability
• Excellent debugging and analytical problem-solving skills, with the ability to investigate complex issues across datasets, model behavior, and production systems
• Strong Python programming skills and experience with modern deep learning frameworks such as PyTorch
• Experience analyzing edge cases, tracing failures to their root cause, and improving model robustness through systematic experimentation
• Excellent communication skills and experience collaborating across cross-functional engineering teams
• Strong understanding of deep learning, sequence modeling, transformers, diffusion models, or other modern ML architectures
• BS, MS, or PhD in Computer Science, Robotics, Machine Learning, or a related field
• Experience with planning, prediction, motion forecasting, or trajectory generation
• Experience deploying machine learning models into production environments
• Working knowledge of modern C++ and production software development
• Experience with TensorRT, ONNX Runtime, CUDA, or ML inference optimization
• Experience with large-scale distributed training or cloud-based ML infrastructure
• Publications or open-source contributions in machine learning, robotics, or autonomous driving
• Candidates who stand out typically have one or more of the following:
• Deep expertise in autonomous vehicle planning or motion prediction
• Experience developing production ML systems for safety-critical applications
• Experience leading technical direction for large ML projects
• Strong intuition for balancing model quality, robustness, latency, and deployment constraints
• Demonstrated ability to solve ambiguous, cross-functional engineering problems involving machine learning, software systems, and autonomous driving