Senior Machine Learning Engineer - Learned Planning/Reinforcement Learning
T
Torc RoboticsAutonomous Vehicles
Remote - U.SFull-TimeSenior
Salary226400 - 271700 USD per year
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Job Details
- Experience
- Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related technical field with 6+ years of industry experience, OR Master’s degree with 3+ years OR PhD with 1+ years of experience
- Required Skills
- PythonPyTorch
Requirements
- Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related technical field with 6+ years of industry experience, OR Master’s degree with 3+ years OR PhD with 1+ years of experience
- Experience applying reinforcement learning, imitation learning, or sequence modeling to robotics, autonomous systems, or complex control problems
- Strong programming skills in Python
- Strong programming skills in PyTorch
- Experience writing production-quality ML code
- Experience training, evaluating, and improving models using large-scale datasets and distributed compute environments
- Solid understanding of ML architectures used in autonomy systems (e.g., transformers, RNNs, graph neural networks, policy networks)
- Experience debugging model behavior, analyzing performance metrics, and improving model reliability
- Ability to translate ambiguous problems into structured ML solutions and deliver results independently
- Experience collaborating cross-functionally to integrate ML models into larger autonomy systems
Responsibilities
- Design, develop, and deploy learned behavior models using approaches such as reinforcement learning, behavior cloning, and imitation learning
- Own end-to-end model development for scoped problem areas, from data ingestion and training to evaluation and deployment
- Write production-quality ML code to support scalable training, evaluation, and inference workflows
- Analyze model performance, identify failure modes, and iterate to improve robustness and generalization across driving scenarios
- Contribute to training pipelines, data workflows, and infrastructure, including working with large-scale datasets from simulation, fleet logs, and on-vehicle data
- Collaborate with simulation, validation, and autonomy teams to test and evaluate learned behavior models across diverse environments
- Support integration of learned planning models into simulation and validation frameworks, enabling faster iteration and improved coverage
- Contribute to model architecture discussions and technical decision-making within the team
- Mentor junior engineers on implementation, experimentation, and best practices
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