Develop and train machine learning models for learned behavior systems, including approaches such as behavior cloning, imitation learning, and reinforcement learning.
Implement production-quality ML code to support model training, evaluation, and inference within the autonomy stack.
Analyze model performance, identify failure modes, and propose improvements to increase robustness and generalization across different scenarios.
Contribute to model training pipelines and data workflows, organizing behavior datasets from simulation, fleet logs, and vehicle data.
Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate learned behavior models across various driving environments.
Help integrate learned behavior models into simulation and testing workflows, enabling faster iteration and more comprehensive validation.
Support the development of tools and infrastructure that improve experiment velocity, reproducibility, and model iteration.
Contribute to technical discussions regarding model architectures and training strategies within the team.

Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or another related technical field with at least 4 years of industry experience, or a Master’s degree with at least 2 years of experience.
Experience applying machine learning techniques such as imitation learning, reinforcement learning, or sequence modeling to robotics, autonomous systems, or complex control environments.
Strong programming skills in Python and PyTorch, with experience writing production-quality ML code.
Experience training and evaluating machine learning models using large datasets and scalable compute environments.
Understanding of ML architectures used in autonomous driving systems, such as transformers, graph neural networks, or sequence models.
Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines.
Ability to collaborate with cross-functional teams to integrate ML models into larger software systems.

Machine Learning Engineer II

Key skills