Machine Learning Engineering Intern - Motion Capture (Fall 2026)

Peripheral is developing spatial intelligence, starting in live sports and entertainment. Our models generate spatial data, used for advanced sports analytics and immersive media experiences. We’re solving key research challenges in 3D computer vision, creating the foundations for the next generation of robotic perception and embodied intelligence.

We’re backed by top investors, including Khosla Ventures, Inovia, Deloitte Ventures, Daybreak, and Entrepreneurs First, and working with some of the biggest names in sports. Our team includes engineers and researchers from leading technology companies and research institutions, and we’re building technology at the intersection of AI, graphics, and the future of live entertainment. We’re ambitious and looking to win.

We're seeking an ML Engineering Intern to join Peripheral's motion capture team, helping deploy, maintain, and improve the systems that power our player pose outputs. Our markerless pose estimation system takes in multiview video and extracts human keypoints, identities, and other spatial information from the scene in real time, and you'll help make that system easier to run in production and better over time.

You'll spend your internship focused on three things: deploying and supporting our models in the cloud, curating and improving the data that trains them, and helping iterate on the models themselves. You'll work closely with the motion capture team's evaluation tools to understand where the system falls short and help close those gaps.

You're currently pursuing a degree in Computer Science, Electrical Engineering, Robotics, or a related field, and you have a strong foundation in 3D computer vision, including camera calibration, multi-view geometry, and 3D coordinate transforms, whether from coursework, research, or personal projects.

You're comfortable with Python and at least one deep learning framework, and you're interested in the practical side of ML: getting models running reliably in production, not just training them.