Robotics ML Ops Intern: Data Tooling & Model Tuning

Apptronik; Austin (TX); On-site; USD 28,000 - 41,000; Full time

Job summary

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 will help close the loop between raw robot experience data and deployable, hardware-ready models for Apollo, Apptronik's humanoid robot.

Qualifications

• Proficient in Python for ML tooling and data pipelines.
• Comfortable with Linux, Git, Docker, and modern Python tooling.
• Hands-on PyTorch experience; quantization/ONNX/TensorRT a plus.
• Robotics coursework or projects with ROS or simulators.
• Experience moving data between annotation, training, evaluation, and storage.
• Prefer experience with annotation workflows or labeling interfaces.
• Familiarity with RL training loops or vision-language-action models is a plus.

Responsibilities

• Design and implement ML data annotation tooling for robot data.
• Profile, quantize, and distill ML models for deployment on hardware.
• Build evaluation harnesses measuring latency, memory, and task success.
• Integrate outputs with S3/MinIO, training pipelines, and Kubernetes.
• Create docs and runbooks for cross-team tooling adoption.

Skills

Python

Linux tools

ML framework

Robotics

Data pipelines

Annotation tooling

RL/VLA exposure

Education

CS/EE/Robotics degree

Tools

Git

Docker

PyTorch

Job description

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 will help close the loop between raw robot experience data and deployable, hardware-ready models for Apollo, Apptronik's humanoid robot.