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.
- 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.
- 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.
Perks of Being a Full-time Torc'r Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:
- 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
For this position, we are open to hiring in Ann Arbor, MI (U.S.) office work locations in a hybrid capacity. We are also open to hiring Remote in the United States.
Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits.
Job ID: 103006
Hiring Range for Job Opening US Pay Range
$153,200-$183,800 USD
