Senior/Staff Machine Learning Engineer, Motion Planning

We’re looking for a machine learning engineer to train and deploy the latest generation of ML-based planning algorithms on the extensive data we collect every day across our autonomous trucking fleet.

Responsibilities

• 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.

Required Skills

• BS, MS, or PhD in Computer Science, Robotics, Machine Learning, or a related field.
• 4+ years of experience developing machine learning systems for robotics, autonomous driving, or other real-time decision-making systems.
• Strong Python programming skills and experience with modern deep learning frameworks such as PyTorch.
• Strong understanding of deep learning, sequence modeling, transformers, diffusion models, or other modern ML architectures.
• Experience designing datasets, experiments, validation methodologies, and metric-driven model evaluation.
• Strong software engineering skills with experience developing and maintaining production-quality software.
• Excellent debugging and analytical problem-solving skills, with the ability to investigate complex issues across datasets, model behavior, and production systems.
• Experience analyzing edge cases, tracing failures to their root cause, and improving model robustness through systematic experimentation.
• Experience designing validation methodologies, automated testing, and monitoring to ensure correctness, safety, and production reliability.
• Strong ownership mindset with the ability to drive problems from investigation through implementation, validation, and deployment.
• Excellent communication skills and experience collaborating across cross-functional engineering teams.

Preferred Skills

• 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.

Bonus Qualifications

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.