Software Engineer Intern: ML Ops at Apptronik in Onsite - Austin, TX

Summary: Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. In this role, you will help build a functional testing pipeline for the full simulation stack, working alongside the simulation engineering and data platform teams to accelerate how Apptronik trains and validates humanoid behaviors. Responsibilities: - Pipeline Development: Build and maintain a containerized functional testing pipeline that executes policies against the simulator in a reproducible, automatable fashion - Functional Test Harness: Implement test scaffolding that detects regressions across changes to robot models (URDF/MJCF/USD), simulator versions, and controller iterations, with baseline comparison and clear pass/fail signals (success rate, object drops, collisions) - Synthetic Data Generation (stretch): Build a producer/consumer data generation flow that captures rollouts from one model and packages them into training-ready datasets for downstream consumers - Integration with Existing Infra: Integrate the pipeline with the team’s existing artifact storage (S3/MinIO), data lake formats (MCAP), and Kubernetes-based execution environment - Documentation & Handoff: Produce design docs, runbooks, and example configurations so the pipeline can be adopted by simulation, controls, and learning teams after the internship Required Qualifications: - Proficiency in Python: Demonstrated ability to write clean, tested, maintainable code for data pipelines, automation, and ML tooling - Linux & Development Tools: Comfortable in a Linux environment; competence with Git, Docker, and modern Python tooling (pytest, uv/poetry, type hints) - Testing & Automation: Experience writing automated tests (unit, integration) and reasoning about determinism, flakiness, and reproducibility - ML / Data Pipeline Exposure: Familiarity with at least one ML framework (PyTorch, TensorFlow) and experience moving data between training, evaluation, and storage stages - CI/CD or Container Orchestration: Exposure to one or more of: GitHub Actions / GitLab CI / Bitbucket Pipelines, Kubernetes, Helm, ArgoCD. Deep expertise not required; willingness to learn the team's stack is - 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 simulation, ML model training, data pipelines, or developer tooling is ideal Preferred Qualifications: - Simulation Familiarity (preferred): Prior exposure to robotic or physics simulators (Isaac Sim / IsaacLab, MuJoCo, Gazebo, or comparable) and to robot description formats (URDF / MJCF / USD) - Reinforcement Learning Exposure (preferred): Familiarity with RL training loops, policy rollouts, or vision-language-action (VLA) models Required Skills: Python, Linux, Git, Docker, pytest, Poetry, Type hints, Automated testing, PyTorch, TensorFlow, GitHub Actions, GitLab CI, Bitbucket Pipelines, Kubernetes, Helm, ArgoCD, Isaac Sim, IsaacLab, MuJoCo, Gazebo, URDF, MJCF, USD, Reinforcement Learning