Machine Learning Engineer II - Learned Planning (Reinforcement Learning)
New
T
TorcAutonomous vehicles
Remote in the United StatesFull-TimeMiddle
Salary$153,200 — $183,800 USD
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Job Details
- Experience
- Bachelor’s degree ... with 4+ years of industry experience, or a Master’s degree with 2+ years of experience.
- Required Skills
- PythonMachine LearningPyTorch
Requirements
- 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 imitation learning, reinforcement learning, or sequence modeling to robotics, autonomous systems, or complex control environments.
- Strong programming skills in Python and PyTorch.
- Experience writing production-quality machine learning code.
- Experience training and evaluating machine learning models using large datasets and scalable compute environments.
- Understanding of autonomy ML architectures such as transformers, graph neural networks, or sequence models.
- Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines.
- Ability to collaborate across teams to integrate ML models into larger software systems.
- Bonus: Experience in autonomous driving, robotics, or simulation-based training environments.
- Bonus: Experience with reinforcement learning frameworks or distributed training systems such as Ray.
Responsibilities
- Develop and train learned behavior models using 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 to robustness and generalization.
- Curate behavior datasets from simulation, fleet logs, and on-vehicle data.
- Collaborate with simulation, validation, and autonomy engineering teams to test learned behavior models across driving environments.
- Integrate learned behavior models into simulation and testing workflows.
- Support tooling and infrastructure for experimentation, reproducibility, and model iteration.
- Contribute to discussions on model architecture and training strategies.
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