Senior, Machine Learning Engineer - Camera Model

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 Senior Machine Learning Engineer – Camera Models, you will develop and deploy machine learning models that power camera-based perception for autonomous trucks. The Camera Models team builds and maintains core vision systems that enable the autonomy stack to understand the environment, detect and localize objects, and estimate scene structure from camera data. Working closely with teams across perception, data, and infrastructure, you will own the development and improvement of robust, scalable camera-based models that support safe and reliable autonomous driving in real-world freight environments. This role focuses on owning model development for scoped problem areas, improving system performance through iteration, and delivering production-ready machine learning solutions within the autonomy stack. What You’ll Do

Design, develop, and deploy deep learning models for camera-based perception (e.g., object detection, segmentation, depth estimation, scene understanding)

Own end-to-end model development for scoped areas, from data curation and training to evaluation and deployment

Write production-quality ML code to support scalable training, evaluation, and inference pipelines

Analyze model performance across diverse driving scenarios, identify failure modes, and improve robustness and generalization

Contribute to and improve large-scale training pipelines, including dataset preparation, distributed training, and experiment tracking

Partner with data teams to improve dataset quality, including labeling strategies and coverage of edge cases

Collaborate with perception, simulation, and validation teams to evaluate and integrate models into the autonomy stack

Improve tooling, workflows, and infrastructure to accelerate experimentation and model iteration

Contribute to model architecture decisions and technical discussions within the team

Mentor junior engineers on implementation, debugging, and best practices

What You’ll Need to Succeed

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 PhD with 1+ years of experience

Experience developing and deploying deep learning models for computer vision or perception systems

Strong programming skills in Python and PyTorch, with experience writing production-quality ML code

Experience training and evaluating models using large-scale datasets and distributed compute environments

Solid understanding of modern deep learning architectures used in perception (e.g., CNNs, transformers, multi-task models)

Experience debugging model behavior, analyzing performance metrics, and improving model reliability

Ability to translate ambiguous problems into structured ML solutions and deliver independently

Experience collaborating cross-functionally to integrate ML models into larger autonomy or robotics systems

Bonus Points:

Experience in autonomous driving, robotics, or simulation-based ML systems

Experience with multi-task learning or unified perception architectures

Experience with large-scale data pipelines, distributed training systems (e.g., Ray), or experiment management frameworks