Senior Machine Learning Engineer - Learned Planning/Reinforcement Learning

Confirmed live in the last 24 hours

Torc Robotics

Compensation

$226,400 - $271,700/year

Remote - U.S, Ann Arbor, MI

Hybrid

Posted April 9, 2026

Job Description

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 – Learned Planner / Reinforcement Learning, you will develop and deploy machine learning models that drive decision-making for autonomous trucks. Working closely with teams across perception, prediction, planning, and safety, you will build learned behavior systems that enable safe, efficient, and human-like driving in real-world freight environments.

This role focuses on owning model development and delivery for scoped problem areas, contributing to architecture decisions, and driving improvements in model performance, reliability, and iteration speed within the autonomy stack.

What You’ll Do

Design, develop, and deploy learned behavior models using approaches such as reinforcement learning, behavior cloning, and imitation learning
Own end-to-end model development for scoped problem areas, from data ingestion and training to evaluation and deployment
Write production-quality ML code to support scalable training, evaluation, and inference workflows
Analyze model performance, identify failure modes, and iterate to improve robustness and generalization across driving scenarios
Contribute to training pipelines, data workflows, and infrastructure, including working with large-scale datasets from simulation, fleet logs, and on-vehicle data
Collaborate with simulation, validation, and autonomy teams to test and evaluate learned behavior models across diverse environments
Support integration of learned planning models into simulation and validation frameworks, enabling faster iteration and improved coverage
Contribute to model architecture discussions and technical decision-making within the team
Mentor junior engineers on implementation, experimentation, and best practices

What You’ll Need to Succeed

Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related technical field with 6+ years of industry experience, OR Master’s degree with 3+ years OR PhD with 1+ years of experience
Experience applying reinforcement learning, imitation learning, or sequence modeling to robotics, autonomous systems, or complex control problems
Strong programming skills in Python and PyTorch, with experience writing production-quality ML code

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