Senior Machine Learning Engineer - Learned Planning/Reinforcement Learning

Torc Robotics is a leader in autonomous driving technology, focused on developing software for automated trucks. As a Senior Machine Learning Engineer, you will design and deploy machine learning models for decision-making in autonomous trucks, collaborating with cross-functional teams to enhance model performance and reliability.

Responsibilities:

• 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

Requirements:

• 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
• Experience 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, scenario generation, 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
• Experience with learned planning systems or policy learning in real-world or simulation environments
• Experience integrating learned behavior models into validation and V&V workflows
• Background in multi-agent modeling, driver behavior modeling, or long-horizon decision-making systems