Machine Learning Engineer

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