Torc Robotics
ML Engineer, II- Learned Behaviors
Remote Machine Learning Engineer role with clear candidate location fit.
PostedJun 22, 2026
Eligible countries2 accepted countries
Seniority signalMiddle
Work settingRemote
Accepted candidate locations
CanadaUSA
Role overview
ML Engineer, II- Learned Behaviors
Requirements and responsibilities
Readable role content extracted into sections for faster review.
About the Company
- Develop and train machine learning models for learned behavior systems, including approaches such as behavior cloning, imitation learning, and reinforcement learning.
About the Company
- Implement production-quality ML code to support model training, evaluation, and inference within the autonomy stack.
About the Company
- Analyze model performance, identify failure modes, and propose improvements to increase robustness and generalization across scenarios.
About the Company
- Contribute to model training pipelines and data workflows, curating behavior datasets from simulation, fleet logs, and on-vehicle data.
About the Company
- Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate learned behavior models across diverse driving environments.
About the Company
- Help integrate learned behavior models into simulation and testing workflows, enabling faster iteration and more comprehensive validation.
About the Company
- Support the development of tooling and infrastructure that improves experimentation speed, reproducibility, and model iteration.
About the Company
- 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.
What You’ll Need to Succeed
- Experience applying machine learning techniques such as imitation learning, reinforcement learning, or sequence modeling to robotics, autonomous systems, or complex control environments.
What You’ll Need to Succeed
- Strong programming skills in Python and PyTorch, with experience writing production-quality ML code.
What You’ll Need to Succeed
- Experience training and evaluating machine learning models using large datasets and scalable compute environments.
What You’ll Need to Succeed
- Understanding of ML architectures used in autonomy systems, such as transformers, graph neural networks, or sequence models.
What You’ll Need to Succeed
- Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines.
What You’ll Need to Succeed
- Ability to collaborate with cross-functional teams to integrate ML models into larger software systems.
What You’ll Need to Succeed
- Experience working in autonomous driving, robotics, or simulation-based training environments.
What You’ll Need to Succeed
- Experience with reinforcement learning frameworks or distributed training systems (e.g., Ray).
What You’ll Need to Succeed
- Experience working with simulation environments or large-scale behavior datasets.
What You’ll Need to Succeed
- Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems.
What You’ll Need to Succeed
- Experience deploying ML models into production or real-world robotics systems.
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