Machine Learning Engineer II - Learned Planning (Reinforcement Learning)

Torc Robotics; United States; Hybrid; USD 153,000 - 184,000; Full time

Benefits offered by this job

Stock options

Medical, dental, vision premiums paid

401K with 6% employer match

Flexible schedule

Paid vacation

AD&D and Life Insurance

Job summary

Torc Robotics is seeking a Machine Learning Engineer II – Learned Behaviors to develop and deploy learned behavior models powering autonomous trucks. You will build and validate ML models, curate data from simulation and field logs, and collaborate with perception, prediction, planning, and safety teams.

You’ll implement production-grade ML code, analyze performance, and help scale training pipelines for robust, real-world operation. Hybrid or remote US work options are available.

Qualifications

• Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry 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.

Responsibilities

• 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.

Skills

Python

PyTorch

Imitation learning

Reinforcement learning

Sequence modeling

Transformers

Graph neural networks

Debugging ML models

Cross-functional collaboration

Production ML code

Education

Bachelor's degree in CS/Robotics/EE/ML (4+ years)

Master's degree in ML/related (2+ years)

Tools

Ray

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 Machine Learning Engineer II – Learned Behaviors, you will help develop anddeploybehaviormodels that power decision-making for autonomous trucks. Working closely with teams acrossperception, 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.

WhatYou’llDo

• 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 aMaster’sdegree 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-timeTorc’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’recommitted to building a diverse and inclusive workplace. We celebrate the uniqueness of ourTorc’rsand 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 youdon’tmeet 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.

$153,200—$183,800 USD