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Senior Machine Learning Engineer - Learned Planning/Reinforcement LearningTORC Robotics • United States
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Senior Machine Learning Engineer - Learned Planning/Reinforcement Learning

TORC Robotics
  • US
    United States
  • US
    United States
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À propos

Senior Machine Learning Engineer - Learned Planning/Reinforcement Learning
Remote - U.S, Ann Arbor, MI 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 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: For this position, we are open to hiring in either the Ann Arbor, MI OR Blacksburg, VA (U.S.) office work locations in a hybrid capacity. We are also open to hiring Remote in the United States Perks of Being a Full-time Torc'r 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 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. 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: 102603 Hiring Range for Job Opening US Pay Range $226,400 - $271,700 USD
  • United States

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