Senior, Machine Learning Engineer - End-to-End

<div> <p><strong>About the Company</strong> </p> <p>At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.</p> <p>A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. <a href="https://torc.ai/daimler-testing-automated-trucks-public/">Now a part of the Daimler family</a>, we are focused solely on developing software for automated trucks to transform how the world moves freight. </p> <p>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. </p> </div> <div><strong>Meet the Team:</strong><br>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.<br><br>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.<br><br>This is a hands-on engineering role focused on execution, iteration, and delivery.<br><br><strong>What You’ll Do</strong></div> <ul> <li>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)</li> <li>Train and evaluate models using large-scale datasets from fleet logs, simulation, and synthetic data</li> <li>Analyze model performance, identify failure modes, and drive data-driven improvements in robustness and generalization</li> <li>Design and refine training pipelines, data workflows, and evaluation strategies to improve iteration speed and model quality</li> <li>Contribute to model architecture decisions, including approaches such as imitation learning, reinforcement learning, transformers, and vision-language-action (VLA) models</li> <li>Collaborate closely with Perception, Prediction, Planning, and Simulation teams to ensure alignment across the autonomy stack</li> <li>Support integration of E2E models into simulation and on-vehicle systems for closed-loop validation</li> <li>Improve tooling, experimentation workflows, and reproducibility across the team</li> <li>Mentor junior engineers and contribute to team-leve