ML Engineer, II - Learned Behaviors

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 autonomy 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.

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 scenarios.

Contribute to model training pipelines and data workflows, curating behavior datasets from simulation, fleet logs, and on-vehicle data.

Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate learned behavior models across diverse driving environments.

Help integrate learned behavior models into simulation and testing workflows, enabling faster iteration and more comprehensive validation.

As a Machine Learning Engineer II – Learned Behaviors, you will help develop and deploy  behavior models that power decision-making for autonomous trucks. Working closely with teams across perception, prediction, planning, and safety, you will contribute to learned behavior modules that enable safe, efficient, and human-like driving in real-world freight operations.

This role focuses on building, validating, and improving machine learning models and infrastructure that support learned behavior systems within the autonomy stack.