Software Engineer Intern - ML Systems

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).

ML Model Optimization

• Profile, quantize, and/or distill ML models (RL policies, VLA controllers, or action heads) to reduce inference latency and memory footprint for deployment on robot hardware.

Hardware-Aware Evaluation

• Build evaluation harnesses that benchmark optimized model performance against baseline, tracking metrics relevant to physical deployment (latency, memory, task success rate).

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.

Documentation & Handoff

• Produce design docs, runbooks, and example configurations so tooling can be adopted by controls, learning, and data platform teams after the internship.

SKILLS AND REQUIREMENTS

• Python Proficiency: Demonstrated ability to write clean, tested, maintainable code for ML tooling, data pipelines, and automation.
• Linux & Development Tools: Comfortable in a Linux environment; competence with Git, Docker, and modern Python tooling (pytest, uv/poetry, type hints).
• ML Framework Experience: Hands-on experience with PyTorch or similar; familiarity with model quantization (INT8/FP16), ONNX export, or TensorRT is a plus.
• Robotics Background: Coursework or project experience with robotic systems — kinematics, control, sensors, or simulation (ROS, MuJoCo, Isaac Sim, Gazebo, or comparable).
• Data Pipeline Exposure: Experience moving data between annotation, training, evaluation, and storage stages.
• Annotation Tooling (preferred): Prior experience with data annotation workflows, labeling interfaces (Label Studio, CVAT, custom tooling), or human-in-the-loop data pipelines.
• Reinforcement Learning or VLA Exposure (preferred): Familiarity with RL training loops, policy rollouts, or vision-language-action (VLA) models.

EDUCATION and/or EXPERIENCE

• Current enrollment in a Bachelor's or Master's degree program in Computer Science, Electrical Engineering, Robotics, or a related field.
• Experience with projects involving robotics, ML model deployment, data annotation, or developer tooling is ideal.

PHYSICAL REQUIREMENTS

• Prolonged periods of sitting at a desk and working on a computer.
• Must be able to lift 15 pounds at times.
• Vision to read printed materials and a computer screen.
• Hearing and speech to communicate.