NVIDIAMLOps Physical AI Data Intern - 2027
NVIDIA is hiring a MLOps Physical AI Data Intern - 2027 in Shanghai, China. Level rates it ; you can apply on Level.
AI in this role
Build and scale distributed data pipelines, LLM models, and AI agents for ground truth generation in physical AI applications.
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.
How we rate this
MLOps Physical AI Data Intern - 2027 at NVIDIA rates 75 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Walk me through a computer vision problem you solved, from raw data to a deployed model.
- Tell me about a project where mlops was part of your work. What did you do?
- Tell me about a project where llm was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI agents, ML Ops, Computer vision, MLOps, and LLM. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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