ML Engineer, II - Learned Behaviors
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About the Role
About the Company
At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.
A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight.
Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.
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.
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