Senior Machine Learning Engineer - Learned Planning/Reinforcement Learning

Torc Robotics; Ann Arbor (MI); Hybrid; USD 226,400 - 271,700; Full time

Benefits offered by this job

Competitive compensation package

100% paid medical, dental, and vision premiums

401(k) plan with 6% employer match

Generous paid vacation

Company-wide holiday office closures

Job summary

A leading autonomous technology company is seeking a Senior Machine Learning Engineer specializing in learned planning and reinforcement learning. Responsibilities include designing and deploying behavior models, enhancing model robustness, and collaborating with cross-functional teams. The ideal candidate holds a relevant degree with significant industry experience in machine learning, specifically in robotics or autonomous systems. The position is hybrid, based in Ann Arbor or Blacksburg, VA, or remote across the U.S., and includes a competitive salary and comprehensive benefits package.

Qualifications

• 6+ years of industry experience, or Master's degree with 3+ years, or PhD with 1+ years.
• Strong programming skills in Python and PyTorch.
• Experience in autonomous driving or robotics is a plus.

Responsibilities

• Design, develop, and deploy learned behavior models using advanced techniques.
• Analyze model performance and iterate to improve robustness.
• Collaborate with teams to integrate ML models into larger systems.

Skills

Reinforcement learning

Imitation learning

Python programming

PyTorch

Cross-functional collaboration

Education

Bachelor's degree in Computer Science, Robotics, Electrical Engineering, or related field

Tools

Distributed computing environments

Reinforcement learning frameworks

Job description

Senior Machine Learning Engineer - Learned Planning/Reinforcement Learning

Responsibilities

• 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
• Experience training, evaluating, and improving models using large‑scale datasets and distributed compute environments
• Solid understanding of ML architectures used in autonomy systems (e.g., transformers, RNNs, graph neural networks, policy networks)
• Experience debugging model behavior, analyzing performance metrics, and improving model reliability
• Ability to translate ambiguous problems into structured ML solutions and deliver results independently
• Experience collaborating cross‑functionally to integrate ML models into larger autonomy systems

Bonus Points

• Experience in autonomous driving, robotics, or simulation‑based training environments
• Experience with reinforcement learning frameworks or distributed training systems (e.g., Ray)
• Experience working with simulation environments, scenario generation, or large‑scale behavior datasets
• Familiarity with vehicle dynamics, motion planning, or multi‑agent decision‑making systems
• Experience deploying ML models into production or real‑world robotics systems
• Experience with learned planning systems or policy learning in real‑world or simulation environments
• Experience integrating learned behavior models into validation and V&V workflows
• Background in multi‑agent modeling, driver behavior modeling, or long‑horizon decision‑making systems

Work Location

Open to hiring in either the Ann Arbor, MI or Blacksburg, VA (U.S.) offices in a hybrid capacity, and open to hiring Remote in the United States.

Perks of Being a Full‑time Torc’r

Torc offers a competitive compensation package that includes a bonus component and stock options, 100 % paid medical, dental, and vision premiums for full‑time employees, a 401(k) plan with a 6 % employer match, flexibility in schedule and generous paid vacation, company‑wide holiday office closures, AD&D and life insurance.

EEO Statement

At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities. Even if you don’t meet 100 % of the qualifications, we encourage you to apply.

Job ID 102603

Hiring Range for Job Opening

US Pay Range: $226,400 – $271,700 USD