Software Engineer Intern - ML Systems

United States·Onsite - Austinentry

OtherSoftware Engineer Intern

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Quick Summary

Key Responsibilities

Design and implement tooling for efficient annotation and curation of robot experience data — including sensor observations, trajectories,

Requirements Summary

Demonstrated ability to write clean, tested, maintainable code for ML tooling, data pipelines, and automation.

Technical Tools

OtherSoftware Engineer Intern

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.

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. 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.

Responsibilities

~1 min read

→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.

Requirements

~2 min read

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.

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.

*This is a direct hire. Please, no outside Agency solicitations.

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.

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.