Senior Machine Learning Engineer - End-to-End

Role 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

Qualifications

• 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.

Benefits

• A competitive compensation package that includes a bonus component and stock options
• 100% paid medical, dental, and vision premiums for full-time employees
• 401K plan with a 6% employer match
• Flexibility in schedule and generous paid vacation (available immediately after start date)
• Company-wide holiday office closures
• AD+D and Life Insurance