Design, develop, and deploy learned behavior models using approaches such as reinforcement learning, behavior cloning, and imitation learning
Own end-to-end model development for scoped problem areas, from data ingestion and training to evaluation and deployment
Write production-quality ML code to support scalable training, evaluation, and inference workflows
Analyze model performance, identify failure modes, and iterate to improve robustness and generalization across driving scenarios
Contribute to training pipelines, data workflows, and infrastructure, including working with large-scale datasets from simulation, fleet logs, and on-vehicle data
Collaborate with simulation, validation, and autonomy teams to test and evaluate learned behavior models across diverse environments
Support integration of learned planning models into simulation and validation frameworks, enabling faster iteration and improved coverage
Contribute to model architecture discussions and technical decision-making within the team
Mentor junior engineers on implementation, experimentation, and best practices

Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related technical field with 6+ years of industry experience, OR Master’s degree with 3+ years OR PhD with 1+ years of experience
Experience applying reinforcement learning, imitation learning, or sequence modeling to robotics, autonomous systems, or complex control problems
Strong programming skills in Python and PyTorch, with experience writing production-quality ML code
Experience training, evaluating, and improving models using large-scale datasets and distributed compute environments
Solid understanding of ML architectures used in autonomy systems (e.g., transformers, RNNs, graph neural networks, policy networks)
Experience debugging model behavior, analyzing performance metrics, and improving model reliability
Ability to translate ambiguous problems into structured ML solutions and deliver results independently
Experience collaborating cross-functionally to integrate ML models into larger autonomy systems

A competitive compensation package that includes a bonus component and stock options
100% paid medical, dental, and vision premiums for full-time employees
401K plan with a 6% employer match
Flexibility in schedule and generous paid vacation (available immediately after start date)
Company-wide holiday office closures
AD+D and Life Insurance

Senior Machine Learning Engineer – Learned Planning, Reinforcement Learning

Key skills