Machine Learning Engineer, II - 3D Perception

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. 

Torc's Multi-Modal Perception team is responsible for developing the machine learning systems that enable our autonomous trucks to perceive and understand the world around them. By combining information from cameras, LiDAR, and other sensor modalities, the team builds production-ready perception capabilities that provide the foundation for safe, reliable autonomous driving.

As a Machine Learning Engineer II – 3D Perception, you'll join a collaborative team of machine learning engineers and researchers focused on solving complex real-world perception challenges. This role is primarily focused on advancing our Bird's Eye View (BEV) perception capabilities by developing, evaluating, and improving production machine learning solutions that support environmental understanding, model robustness, and system performance across Torc's autonomy stack.

• →Design, develop, and improve machine learning models supporting Torc's perception systems.
• →Own model development and delivery for well-defined perception problem areas, from data preparation and training through evaluation and integration.
• →Write production-quality Python and PyTorch code to support scalable training, evaluation, and inference workflows.
• →Analyze model performance, identify failure modes, and independently troubleshoot issues to improve robustness, accuracy, and generalization.
• →Develop and evaluate perception models leveraging multi-modal sensor data, with an emphasis on camera-based and 3D perception systems.
• →Collaborate with software engineers, infrastructure teams, and autonomy engineers to integrate perception models into larger production software systems.
• →Contribute to improvements in training pipelines, data workflows, experimentation tooling, and developer workflows that accelerate model iteration and deployment.
• →Participate in model architecture discussions and contribute technical recommendations within the team.
• →Lead small technical initiatives or model components with guidance from senior engineers.
• →Support and mentor Machine Learning Engineer I team members on implementation, experimentation, and machine learning best practices.
• →Document technical work, evaluation results, and design decisions to support knowledge sharing and long-term maintainability.

What You'll Need to Succeed

• →Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 3+ years of relevant industry experience, OR Master's degree with 1+ years of relevant experience, or equivalent practical experience.
• →Experience developing machine learning models for computer vision, perception, robotics, autonomous systems, or a closely related domain.
• →Strong programming skills in Python and PyTorch, with experience writing maintainable, production-quality machine learning code.
• →Experience training, evaluating, and improving deep learning models using large-scale datasets.
• →Experience working with image-based and/or 3D perception systems.
• →Solid understanding of deep learning architectures commonly used for perception applications.
• →Experience debugging model behavior, analyzing performance metrics, and proposing practical improvements.
• →Ability to independently execute complex machine learning work within well-defined problem areas.
• →Experience collaborating cross-functionally to integrate machine learning models into larger software systems.
• →Strong problem-solving skills with the ability to operate effectively in an environment with evolving technical challenges and requirements.

Bonus Points

• →Experience developing perception systems for autonomous driving, robotics, or ADAS.
• →Experience with LiDAR, point cloud processing, sensor fusion, BEV representations, or other 3D perception techniques.
• →Experience with temporal perception models or video-based learning.
• →Experience with C++, ROS, or robotics software development.
• →Experience deploying machine learning models into production autonomy or robotics platforms.
• →Experience working with large-scale perception datasets and distributed training environments.
• →Familiarity with perception evaluation frameworks, model validation, and performance benchmarking.
• →Experience improving ML tooling, automation, training workflows, or experimentation infrastructure.
• →Experience leading a small technical initiative or owning a production ML component from development through deployment.