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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - End-to-End (E2E) - **Company:** Torc Robotics, Inc. - **Location:** Ann Arbor, MI, United States (Remote available) - **Experience:** Expert - **Salary:** $226,400.0 - $271,700.0 - **Contract:** Permanent contract - **Skills:** Big Data, Computer Programming, Software Debugging, Distributed Computing Environment, Python (Programming Language), Machine Learning, Language Modeling, Motion Planning, Reinforcement Learning, Pytorch, Multi-Agent Systems, Information Technology, Data Analytics, Machine Learning Operations, Lidar - **Published:** June 24, 2026 - **Apply:** https://www.dice.com/job-detail/b5fffc22-f8d3-450f-9e22-0ec4fa0d5e1b ## About the Role * 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 open to hiring in Ann Arbor, MI (U.S.) office work locations in a hybrid capacity. We are also open to hiring Remote in the United States. ## Description 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 ## Related Videos - [How to develop an autonomous car end-to-end: Robotic Drive and the mobility revolution](https://www.wearedevelopers.com/videos/22-how-to-develop-an-autonomous-car-end-to-end-robotic-drive-and-the-mobility-revolution) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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