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ML Engineer, II - Learned Behaviors at Torc Robotics. . Location: Remote - US, Ann Arbor, MI, Montreal, Canada, Remote - Canada. Meet the Team:.  . 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..  .  . What You’ll Do.  . . . 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..  . . . . Support the development of tooling and infrastructure that improves experimentation speed, reproducibility, and model iteration..  . . . . Contribute to technical discussions around model architecture and training strategies within the team..  . . .  .  .  .  . What You’ll Need to Succeed.  . . . Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 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 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..  . . .  .  . Bonus Points!.  . . . Experience working in autonomous driving, robotics, or simulation-based training environments..  . . . . Experience with reinforcement learning frameworks or distributed training systems (e.g., Ray)..  . . . . Experience working with simulation environments or large-scale behavior datasets..  . . . . Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems..  . . . . Experience deploying ML models into production or real-world robotics systems..  . . .