MLOps Physical AI Data Intern - 2027
China, Shanghai
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s listing: Full time
From the employer’s posting
What you’ll work on
Full postingContribute to distributed data pipelines for large-scale GT generation
Integrate international vision, LiDAR, map and sensor datasets
Develop GT auto labeling, LLM models and AI agents
Help design tools for monitoring data quality and pipeline progress
Improve platform scalability, performance and workflow automation
From the employer’s posting
Contribute to the development of distributed data pipelines supporting large-scale GT generation
Support the integration of international datasets (vision, LiDAR, map, and sensor)
Develop GT auto labeling, LLM models, and a variety of AI Agents for the global GT generation
Help design tools for monitoring data quality, job status, and progress tracking
Participate in continuous improvement initiatives for platform scalability, performance tuning, and workflow automation
Education & alternatives
What We Need to See: - 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++.
Tools in this posting
- Python
- C++
Source — Tool mentions in context
- 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.
Posting record
Report an errorActively hiringFirst seen by us 9 days ago, still listed, and not a repost of an earlier listing.
- Status in our records
- Live
- First seen by us
- Oct 12, 2026 · 9 days ago
- Recorded sightings
- 33
- Last seen by us
- Oct 21, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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- Have your resume and examples of relevant work ready.
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Job description
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're 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.
What You’ll Be Doing:
- Contribute to the development of distributed data pipelines supporting large-scale GT generation and global data distribution.
- Support the integration of international datasets (vision, LiDAR, map, and sensor) for multi-region model development and validation.
- Develop GT auto labeling, LLM models, and a variety of AI Agents for the 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.
What We Need to See:
- 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.
- 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.
Ways to Stand Out from the crowd:
- Proficient in developing AI agents.
- Experience in Physical AI data engineering, or MLOps.
- Prior exposure to autonomous driving data, computer vision, or machine learning pipelines.
- Hands-on experience with Physical AI modeling, GT data processing.
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.
Source details
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- Pay
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- Location & working pattern
China, Shanghai
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