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.
Qualification
Bachelor's degree in Computer ScienceExperience trainingExperience debugging model behaviorExperience with LiDARExperience with C++Experience improving ML tooling
Required
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.
Preferred
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.
