Machine Learning Engineer II - Learned Planning (Reinforcement Learning)

Torc Robotics; Ann Arbor (MI); Hybrid; USD 153,000 - 184,000; Full time

Benefits offered by this job

Bonus component

Stock options

Medical coverage

401K match

Flexible vacation

AD+D Insurance

Job summary

Torc Robotics is seeking a Machine Learning Engineer II – Learned Behaviors to help develop and deploy behavior models powering autonomous trucks. You will work with perception, prediction, planning, and safety teams to create safe, efficient, human‑like driving in real‑world freight operations.

You will build, validate, and improve ML models and infrastructure for learned behavior systems within the autonomy stack, partnering with simulation and autonomy engineering teams to ensure robust

Qualifications

• Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related field with 4+ years of industry experience, or Master’s with 2+ years.
• Experience applying ML techniques such as imitation learning or reinforcement learning to autonomous systems.
• Strong Python and PyTorch skills with production‑quality ML code experience.
• Experience training/evaluating ML models on large datasets with scalable compute.
• Understanding ML architectures used in autonomy, such as transformers or graph networks.
• Ability to debug model behavior and iterate on training pipelines.

Responsibilities

• Develop and train ML models for learned behavior systems (behavior cloning, imitation, RL).
• Implement production‑quality ML code for training, evaluation, and inference.
• Analyze model performance, identify failures, and improve robustness across scenarios.
• Contribute to model training pipelines and data workflows (simulation, logs, on‑vehicle data).
• Collaborate with teams to test models across diverse driving environments.
• Integrate learned behavior models into simulation/testing workflows for faster iteration.
• Support tooling and infrastructure to improve experimentation speed and reproducibility.
• Participate in discussions on model architecture and training strategies.

Skills

Python

PyTorch

Machine learning

Reinforcement learning

Sequence modeling

Autonomous systems

Data pipelines

Production ML

Education

Bachelor’s degree in CS/Robotics/EE/ML

Master’s degree

Tools

Ray

Simulation tools

Git

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.

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 Machine Learning Engineer II – Learned Behaviors, you will help develop and deploy behavior models that power decision‑making for autonomous trucks. Working closely with teams across perception, prediction, planning, and safety, you will contribute to learned behavior modules that enable safe, efficient, and human‑like driving in real‑world freight operations.

This role focuses on building, validating, and improving machine learning models and infrastructure that support learned behavior systems within the autonomy stack.

What You’ll Do

• Develop and train machine learning models for learned behavior systems, including approaches such as behavior cloning, imitation learning, and reinforcement learning.
• Implement production‑quality ML code to support model training, evaluation, and inference within the autonomy stack.
• Analyze model performance, identify failure modes, and propose improvements to increase robustness and generalization across scenarios.
• Contribute to model training pipelines and data workflows, curating behavior datasets from simulation, fleet logs, and on‑vehicle data.
• Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate learned behavior models across diverse driving environments.
• Help integrate learned behavior models into simulation and testing workflows, enabling faster iteration and more comprehensive validation.
• Support the development of tooling and infrastructure that improves experimentation speed, reproducibility, and model iteration.
• Contribute to technical discussions around model architecture and training strategies within the team.

What You’ll Need To Succeed

• Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 2+ years of experience.
• Experience applying machine learning techniques such as imitation learning, reinforcement learning, or sequence modeling to robotics, autonomous systems, or complex control environments.
• Strong programming skills in Python and PyTorch, with experience writing production‑quality ML code.
• Experience training and evaluating machine learning models using large datasets and scalable compute environments.
• Understanding of ML architectures used in autonomy systems, such as transformers, graph neural networks, or sequence models.
• Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines.
• Ability to collaborate with cross‑functional teams to integrate ML models into larger software systems.

Bonus Points!

• Experience working 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 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.

Perks of Being a Full‑time Torc’r

Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. 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
• 401K plan with a 6% employer match
• Flexibility in schedule and generous paid vacation (available immediately after start date)
• AD+D and Life Insurance

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 listed for this opportunity, we encourage you to apply.

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.

Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job‑related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered, sign‑on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits.

Job ID: 103006

Hiring Range for Job Opening

US Pay Range

$153,200—$183,800 USD