MLOps Physical AI Data Intern - 2027
Salary not listed • China, Shanghai • Internship
Posted 19d ago
Remote Work Policy
On-site
Employment Type
Internship
Categories
Machine Learning Engineer
About the job
NVIDIA is building the next generation of AI and advanced data technologies for Physical AI. Our MLOps GT team designs large-scale data infrastructure, LLM models and agents powering Ground Truth (GT) generation, and end-to-end AI workflows for global automotive and robotic partners. We are looking for a motivated MLOps Physical AI Data Intern to help build, scale, and optimize this global data ecosystem while driving strong collaboration across engineering, operations, and research teams. At NVIDIA, interns are integral to real-world projects that push the boundaries of Physical AI. You’ll gain hands-on experience in global-scale data operations, work with exceptional engineers, scientists, and play a role in enabling next-generation autonomous driving systems across the world.
Responsibilities
- Contribute to developing distributed data pipelines for large-scale GT generation and global data distribution.
- Support the integration of international datasets (vision, LiDAR, map, sensor) for multi-region model development and validation.
- Develop GT auto labeling, LLM models, and AI Agents for global GT generation.
- Help design tools for monitoring data quality, job status, and progress tracking within the global GT release pipeline.
- Participate in continuous improvement initiatives for platform scalability, performance tuning, and workflow automation.
Requirements
- Currently pursuing a BS, MS, or PhD in Computer Science, Software Engineering, Data Engineering, or a related field.
- Solid programming experience with Python and C++.
- Good understanding of computer vision, AI agent development, LLM, and machine learning.
- Analytical and problem-solving abilities with an interest in large-scale AI data ecosystems.
- Good communication and teamwork skills, especially for collaboration across global and multi-disciplinary teams.