Autonomy Software Engineering Internship, World Modeling

• Job Title: Autonomy Software Engineering Intern, World Modeling
• Department: Robotics Software Engineering
• Employment Type: Internship
• Location: Houston, TX – Onsite
• Design and implement autonomy and behavior coordination algorithms for an industrial humanoid platform
• Work with manipulation, locomotion, and perception engineers to integrate robot skills into autonomous robot behaviors
• Implement data curation pipelines that enable our robots to understand a variety of real-world industrial tasks.
• Develop synthetic data generation pipelines for post-training of humanoid world models
• Design and implement pipelines for autonomous behavior evaluation, benchmarking, and regression testing
• Turn raw industrial video footage (shipyards, steel fabrication, welding cells) into structured, high-quality training data.
• Design and evaluate reasoning benchmarks that measure how well a fine-tuned world model grounds language queries in real shipyard and fabrication-plant scenes.
• Currently pursuing a BS, MS, or PhD in Robotics, Computer Science, Machine Learning, Electrical Engineering, or a related field; recent graduates are also welcome.
• Hands-on experience training, fine-tuning, or evaluating modern deep learning models in PyTorch.
• Strong Python skills; comfortable writing data processing scripts, training loops, and evaluation harnesses.
• Working familiarity with at least one vision-language model, large language model, or video understanding model.
• Practical experience building data pipelines: ingestion, filtering, labeling workflows, deduplication, train/val/test splits.
• Comfortable working in Linux environments with Git, containers (Docker), and cloud GPU infrastructure.
• Self-directed and able to make steady progress on an open-ended research problem with weekly check-ins rather than daily direction.
• Direct experience with NVIDIA Cosmos or comparable world foundation models.
• Exposure to NVIDIA Isaac Sim, Omniverse, or OpenUSD for synthetic data generation and scene variability.
• Familiarity with humanoid or mobile-manipulator robotics, ROS/ROS 2, and the broader autonomy stack.
• Experience with motion capture data, egocentric video capture, or first-person dataset construction.
• Exposure to behavior trees, task and motion planning, or LLM-driven planning architectures.
• Familiarity with NVIDIA Isaac Lab, NIM microservices, or the Hugging Face training ecosystem.

None flagged.