ML Engineer, II - Learned Behaviors

About the Role

The autonomy stack is a complex system that requires collaboration and communication between various teams. As an ML Engineer, II, you will work closely with the perception, prediction, planning, and safety teams to ensure seamless integration of the learned behavior models. Your expertise in machine learning, Python, and PyTorch will be essential in developing and deploying these models.

The role of the ML Engineer, II is crucial in the development of autonomous trucks. With the rise of autonomous vehicles, the demand for skilled engineers who can develop and deploy behavior models has increased significantly. As a pioneer in autonomous driving, Torc Robotics is at the forefront of this technology, and this role offers a unique opportunity to be part of a team that is shaping the future of transportation.

What You Will 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.
• Stay up-to-date with the latest developments in machine learning and autonomous vehicles, applying this knowledge to improve the performance of the learned behavior models.

What We Are Looking For

• 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.
• Strong communication and problem-solving skills, with the ability to work effectively in a team environment.

Nice to Have

• 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.

Benefits and Perks

• Competitive salary and benefits package.
• Opportunity to work on cutting-edge technology with a pioneering company in the autonomous vehicle industry.
• Collaborative and dynamic work environment with a team of experienced engineers and researchers.
• Flexible working hours and remote work options.
• Professional development opportunities, including training and conference attendance.
• Access to the latest tools and technologies in machine learning and autonomous vehicles.
• A culture that values innovation, teamwork, and continuous learning.

• Ensure you have a strong foundation in machine learning and Python, with experience in PyTorch and production-quality ML code.
• Familiarize yourself with the latest developments in autonomous vehicles and machine learning, and be prepared to discuss your knowledge and experience in these areas.
• Be prepared to provide examples of your experience with machine learning models, including development, training, and deployment.
• Highlight your ability to collaborate with cross-functional teams and communicate complex technical concepts effectively.
• Consider creating a portfolio or GitHub repository showcasing your projects and experience in machine learning and autonomous vehicles.
• Prepare to discuss your experience with reinforcement learning, imitation learning, and other relevant machine learning techniques.
• Be prepared to negotiate your salary and benefits package, and consider factors such as remote work options, flexible working hours, and professional development opportunities.