Apptronik is an Austin, Texas-based humanoid robotics company founded in 2016 as a spinout from the Human Centered Robotics Lab at the University of Texas at Austin. The company designs and builds general-purpose humanoid robots for industrial applications, with its flagship product Apollo deployed across logistics, manufacturing, and retail sectors for tasks such as trailer unloading, case picking, palletization, machine tending, and workcell delivery.
<div class="content-intro"><p>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 built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond.<br><br>We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better.</p></div><p><strong>JOB SUMMARY</strong><br>Apptronik is seeking a Software Engineering Intern to join our ML Ops team for a 12-week fall project. In this role, you will work at the intersection of robotics and applied machine learning — building data annotation tooling and optimizing ML models that run on humanoid hardware. You<br>will help close the loop between raw robot experience data and deployable, hardware-ready models for Apollo, Apptronik’s humanoid robot.</p> <p>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, and contribute directly to how Apptronik turns ML research into production robot behavior.</p> <p><strong>ESSENTIAL DUTIES AND RESPONSIBILITIES</strong></p> <ul> <li>Data Annotation Tooling: Design and implement tooling for efficient annotation and curation of robot experience data — including sensor observations, trajectories, and task outcomes — in formats compatible with the team’s data lake (MCAP, S3/MinIO).</li> <li>ML Model Optimization: Profile, quantize, and/or distill ML models (RL policies or action heads) to reduce inference latency and memory footprint for deployment on robot hardware.</li> <li>Hardware-Aware Evaluation: Build evaluation harnesses that benchmark optimized model performance against baseline, tracking metrics relevant to physical deployment (latency, memory, task success rate).</li> <li>Integration with Existing Infra: Connect annotation outputs and optimized model artifacts with the team’s existing artifact storage (S3/MinIO), training pipelines, and Kubernetes-based execution environment.</li> <li>Documentation &amp; Handoff: Produce design docs, runbooks, and example configurations so tooling can be adopted by controls, learning, and data platform teams after the internship.</li> </ul> <p><strong>SKILLS AND REQUIREMENTS</strong></p> <ul> <li>Python Proficiency: Demonstrated ability to write clean, tested, maintainable code for ML tooling, data pipelines, and automation.</li> <li>Linux &amp; Development Tools: Comfortable in a Linux environment; competence with Git, Docker, and modern Python tooling (pytest, uv/poetry, type hints).</li> <li>ML Framework Experience: Hands-on experience with PyTorch or similar; familiarity with model quantization (INT8/FP16), ONNX export, or TensorRT is a plus.</li> <li>Robotics Background: Coursework or project experience with robotic systems — kinematics, control, sensors, or simulation (ROS, MuJoCo, Isaac Sim, Gazebo, or comparable).</li> <li>Data Pipeline Exposure: Experience moving data between annotation, training, evaluation, and storage stages.</li> <li>Annotation Tooling (preferred): Prior experience with data annotation workflows, labeling interfaces (Label Studio, CVAT, custom tooling), or human-in-the-loop data pipelines.</li> <li>Reinforcement Learning o: Familiarity with RL training loops, policy rollouts models.</li> </ul> <p><strong>EDUCATION and/or EXPERIENCE</strong></p> <ul> <li>Current enrollment in a Bachelor’s or Master’s degree program in Computer Science, Electrical Engineering, Robotics, or a related field.</li> <li>Experience with projects involving robotics, ML model deployment, data annotation, or<br>developer tooling is ideal.</li> </ul> <p><strong>PHYSICAL REQUIREMENTS</strong></p> <ul> <li>Prolonged periods of sitting at a desk and working on a computer.</li> <li>Must be able to lift 15 pounds at times.</li> <li>Vision to read printed materials and a computer screen.</li> <li>Hearing and speech to communicate.</li> </ul><div class="content-conclusion"><p> </p> <p> </p> <p>*This is a direct hire. Please, no outside Agency solicitations. </p> <p class="p1">Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.</p></div>