Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is designed to collaborate with people in industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of embodied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. We are seeking a Software Engineering Intern to join our ML Ops team for a 12-week fall project. Based in Austin, Texas, this on-site role will place you at the intersection of robotics and applied machine learning—building data annotation tooling and optimizing ML models that run on humanoid hardware. You will take ownership of two interconnected workstreams: (1) building or extending data annotation tools that let the team efficiently label and curate robot experience data, and (2) applying ML model optimization techniques—quantization, distillation, and inference profiling—to improve the throughput and efficiency of models deployed on physical systems. You will work alongside the simulation engineering, data platform, and learning teams, contributing to how Apptronik turns ML research into production robot behavior. Essential duties include designing annotation tooling for efficient labeling and curation of robot data (sensor observations, trajectories, task outcomes) in formats compatible with the team's data lake (MCAP, S3/MinIO); profiling, quantizing, and distilling ML models (RL policies, VLA controllers, or acti

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is designed to collaborate with people in industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of embodied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. We are seeking a Software Engineering Intern to join our ML Ops team for a 12-week fall project. Based in Austin, Texas, this on-site role will place you at the intersection of robotics and applied machine learning—building data annotation tooling and optimizing ML models that run on humanoid hardware. You will take ownership of two interconnected workstreams: (1) building or extending data annotation tools that let the team efficiently label and curate robot experience data, and (2) applying ML model optimization techniques—quantization, distillation, and inference profiling—to improve the throughput and efficiency of models deployed on physical systems. You will work alongside the simulation engineering, data platform, and learning teams, contributing to how Apptronik turns ML research into production robot behavior. Essential duties include designing annotation tooling for efficient labeling and curation of robot data (sensor observations, trajectories, task outcomes) in formats compatible with the team's data lake (MCAP, S3/MinIO); profiling, quantizing, and distilling ML models (RL policies, VLA controllers, or acti