Torc Robotics - ML Engineer, II - End to End (E2E)
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Requirements
• Experience working in autonomous driving, robotics, or simulation-based training environments. • Experience with reinforcement learning frameworks or distributed training systems (e.g., Ray). • Experience with VLA or Neural Rendering. • Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems. • Experience deploying ML models into production or real-world robotics systems.
Responsibilities
• Develop and train machine learning models for End-to-End percetion and planning, including approaches such as 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 datasets from simulation, fleet logs, and on-vehicle data. • Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate End-to-End models across diverse driving environments. • Help integrate End-to-End models into simulation and testing workflows, enabling faster iteration and more comprehensive validation. • Support the development of tooling and infrastructure that improve experimentation speed, reproducibility, and model iteration. • Contribute to technical discussions around model architecture and training strategies within the team.
Benefits
• $153,200 - $183,300 USD
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