Parallel Systems is building autonomous battery-electric rail vehicles to transform freight transportation. This role focuses on designing and deploying deep learning perception systems, including object detection, segmentation, tracking, and scene understanding for real-world rail environments. You will own the full ML lifecycle, from data pipelines and model training to production deployment, and collaborate with autonomy, robotics, and systems teams.
Responsibilities
Design, develop, and deploy advanced machine learning models for large-scale perception problems.
Own the full ML lifecycle from data mining and annotation to training, evaluation, and deployment.
Build and optimize deep learning architectures for object detection, segmentation, tracking, pose estimation, and scene understanding.
Develop scalable and efficient training pipelines for robust real-time inference.
Work extensively with large image, video, lidar, and radar datasets.
Conduct research on new architectures and integrate state-of-the-art methods.
Build ML infrastructure and tools for data labeling, training, evaluation, and model versioning.
Collaborate cross-functionally to integrate ML systems into real-world applications.
Requirements
Bachelor's or higher degree in Computer Science, Machine Learning, or related field.
4+ years of hands-on experience developing and deploying ML systems at scale.
Parallel Systems is a railroad equipment manufacturing company that develops autonomous, battery-electric rail vehicles. Its technology is designed to move freight from trucks to rail, aiming to reduce highway congestion, lower shipping costs, and decrease pollution.