Senior, Machine Learning Engineer - End-to-End

Remote - USfull timeRemote

Apply on Employer Site

senior

$226k–$272k

H1B: yes

Torc Robotics Senior Machine Learning Engineer at Torc Robotics developing and scaling end-to-end ML models for autonomous trucks. Focuses on model performance, training pipelines, and cross-functional collaboration across perception, prediction, and planning teams.

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

Qualification

Bachelor’s degree with 6+ yearsProven ability to debug model behaviorUnderstanding of vehicle dynamicsWork Location

Required

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

Preferred

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.