Senior, Machine Learning Engineer - End-to-End

Torc Robotics is a leader in autonomous driving technology, focused on developing software for automated trucks. The Senior Machine Learning Engineer will develop and scale learning-based systems to enhance driving behavior and ensure safe, efficient autonomy for freight operations.

Responsibilities:

• Own development and delivery of End-to-End ML models that map multi-modal sensor inputs (camera, LiDAR, radar, maps) to driving-relevant outputs (trajectories, cost functions, or intermediate representations)
• Train and evaluate models using large-scale datasets from fleet logs, simulation, and synthetic data
• Analyze model performance, identify failure modes, and drive data-driven improvements in robustness and generalization
• Design and refine training pipelines, data workflows, and evaluation strategies to improve iteration speed and model quality
• Contribute to model architecture decisions, including approaches such as imitation learning, reinforcement learning, transformers, and vision-language-action (VLA) models
• Collaborate closely with Perception, Prediction, Planning, and Simulation teams to ensure alignment across the autonomy stack
• Support integration of E2E models into simulation and on-vehicle systems for closed-loop validation
• Improve tooling, experimentation workflows, and reproducibility across the team
• Mentor junior engineers and contribute to team-level best practices and technical discussions

Requirements:

• Bachelor's degree with 6+ years, Master's with 4+ years, or PhD with 0–2 years of experience in Machine Learning, Robotics, Computer Science, or a related field with a track record of publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL)
• Experience developing and deploying ML models for autonomous systems, robotics, or complex decision-making environments
• Strong programming skills in Python and PyTorch, with ability to write production-quality ML code
• Experience training and evaluating models using large-scale datasets and distributed compute environments
• Solid understanding of ML architectures used in E2E systems, such as Transformers, BEV models, VLA/VLM approaches, or diffusion models
• Proven ability to debug model behavior, analyze performance metrics, and drive iterative improvements
• Experience contributing to or influencing model architecture and training strategies
• Ability to work cross-functionally and integrate ML systems into larger autonomy pipelines
• Experience developing End-to-End or mid-to-end models for autonomous driving or robotics
• Experience with vision-language models (VLMs) or vision-language-action (VLA) systems
• Familiarity with closed-loop simulation and evaluation frameworks
• Experience with reinforcement learning or imitation learning in real-world systems
• Experience with distributed training frameworks (e.g., Ray)
• Understanding of vehicle dynamics, motion planning, or multi-agent systems