Senior, Machine Learning Engineer - Camera Model

Torc Robotics develops software for automated trucks and autonomous driving systems. The Senior Machine Learning Engineer will develop, deploy, and improve camera-based deep learning models for autonomous vehicle perception, owning model development from data curation and training through evaluation and production deployment. The role also involves collaborating across perception, data, simulation, validation, and infrastructure teams and mentoring junior engineers.

Responsibilities:

• 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

Requirements:

• 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
• 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
• Familiarity with camera calibration, geometric reasoning, or 3D perception from images (e.g., BEV, monocular depth, structure-from-motion)
• Experience deploying ML models into production or real-world robotics systems