Machine Learning Engineer II - Learned Planning (Reinforcement Learning)

Summary

Torc Robotics develops software for autonomous trucks and focuses on commercializing autonomous vehicle technology. The Machine Learning Engineer II will develop, train, validate, and deploy learned behavior models using behavior cloning, imitation learning, and reinforcement learning to support decision-making in autonomous trucks. The role also involves building ML infrastructure, analyzing model performance, curating datasets, and collaborating across autonomy, simulation, validation, and engineering teams.

Responsibilities

• 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

Skills

• 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
• 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

Qualifications

Must Haves

• 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

Nice to Haves

• 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

Benefits

• 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)
• AD+D and Life Insurance
• Hybrid work capacity in Ann Arbor, MI (U.S.)
• Remote work in the United States
• Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package