Senior, Machine Learning Engineer - 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 Senior Machine Learning Engineer – 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, developing production machine learning solutions that improve environmental understanding, model robustness, and system performance across Torc's autonomy stack.

• →Design, develop, and deploy production machine learning models supporting Torc's perception systems.
• →Own end-to-end model development for scoped perception problem areas, from data preparation and training through 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, accuracy, and generalization across diverse driving environments.
• →Develop and optimize 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 production autonomy software.
• →Support improvements to training pipelines, data workflows, and tooling that accelerate model iteration and deployment.
• →Contribute to model architecture discussions and technical decision-making within the team.
• →Mentor junior engineers on implementation, experimentation, and machine learning best practices.

• Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 6+ years of industry experience, OR Master's degree with 3+ years, OR Ph.D. with 1+ years of experience.
• Experience developing production machine learning models for computer vision, perception, robotics, or autonomous systems.
• Strong programming skills in Python and PyTorch, with experience writing production-quality ML code.
• Experience training, evaluating, and improving deep learning models using large-scale datasets.
• Experience working with image-based and/or 3D perception systems.
• Strong understanding of deep learning architectures commonly used for perception applications.
• Experience debugging model behavior, analyzing performance metrics, and improving model reliability.
• Ability to translate ambiguous perception challenges into practical machine learning solutions and deliver results independently.
• Experience collaborating cross-functionally to integrate ML models into larger software systems.

• Experience developing perception systems for autonomous driving or robotics.
• Experience with LiDAR, point cloud processing, sensor fusion, 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, infrastructure, or developer workflows to accelerate model iteration.