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# Machine Learning Engineer II

- **Company:** Torc Robotics, Inc.
- **Location:** Ann Arbor, MI, United States
- **Experience:** Experienced
- **Salary:** $153,200.0 - $183,800.0
- **Contract:** Permanent contract
- **Skills:** Big Data, Computer Programming, Software Debugging, Distributed Computing Environment, Python (Programming Language), Machine Learning, Motion Planning, Software Systems, Reinforcement Learning, Graph Neural Networks, Pytorch, Multi-Agent Systems, Information Technology, Performance Monitor, Machine Learning Operations, Recurrent Neural Networks
- **Published:** October 1, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-ii-learned-planning-reinforcement-learning-torc-robotics-10238610

## About the Role

* 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.

## Description

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.