Machine Learning Engineer II - Learned Planning (Reinforcement Learning)
$153k - $184k • Remote - US, Ann Arbor, MI • Full-time
Posted 1mo ago
Job Location
Remote - US, Ann Arbor, MI
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
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.
Responsibilities
- Develop and train machine learning models for learned behavior systems, including behavior cloning, imitation learning, and reinforcement learning.
- Implement production-quality ML code for model training, evaluation, and inference within the autonomy stack.
- Analyze model performance, identify failure modes, and propose improvements for robustness and generalization.
- 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.
- Integrate learned behavior models into simulation and testing workflows for faster iteration and validation.
- Support the development of tooling and infrastructure to improve experimentation speed, reproducibility, and model iteration.
- Contribute to technical discussions on model architecture and training strategies.
Requirements
- Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related technical field with 4+ years of industry experience, or Master's degree with 2+ years of experience.
- Experience applying machine learning techniques like 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 ML models using large datasets and scalable compute environments.
- Understanding of ML architectures used in autonomy systems (e.g., transformers, graph neural networks, 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.
Benefits
- Competitive compensation package including 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
- AD+D and Life Insurance