Senior, Machine Learning Engineer - End-to-End

About the Company At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight.

Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer. Meet the Team: As a Senior Machine Learning Engineer – End-to-End (E2E), you will develop and scale learning-based systems that connect multi-modal perception inputs to driving behavior, enabling safe, efficient, and human-like autonomy for real-world freight operations. You’ll work at the intersection of perception, prediction, and planning, contributing to unified learning pipelines that operate in closed-loop environments.

This role focuses on owning meaningful portions of the E2E stack, improving model performance at scale, and driving iteration through data, experimentation, and cross-functional collaboration. This is a hands-on engineering role focused on execution, iteration, and delivery.

What You’Ll Do

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 What You’ll Need to Succeed 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 Bonus Points 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 Work Location: For this position, we are