Senior Physical AI Data Engineer
NVIDIA is hiring a Senior Physical AI Data Engineer in Shanghai, China. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- Level rates this data engineer role Builds AI ●●●●: the job is building AI systems.
- The posting mentions AI agents, MLOps and computer vision among its tools and skills.
- Level classes it as a senior role.
- NVIDIA has 2,700+ other open roles on Level.
- Posted in the last few days.
AI in this role
NVIDIA is building the next generation of AI and advanced data technologies for Physical AI. Our MLOps Ground Truth (GT) team develops large-scale data infrastructure, tooling, AI-based GT generation systems, DNN auto labeling, VLM labeling, and agentic workflows for global automotive and robotics partners.
We are looking for a Senior Physical AI Data Engineer to architect, build, and scale this global data ecosystem. In this role, you will drive the development of large scale automated labeling systems, and LLM- and agent-powered workflows that transform multimodal sensor data into high-quality datasets for model training and validation. You will collaborate closely with human operations and research teams to deliver reliable, scalable and ultra high quality GT data for autonomous driving and robotics.
What You’ll Be Doing
- Develop and productionize AI-powered auto-labeling workflows using computer vision models, vision-language models, and AI agents, integrating human review where needed.
- Design and implement MLOps capabilities for reproducible training and inference, model and dataset versioning, evaluation, deployment, and lifecycle management.
- Build observability and data quality frameworks covering pipeline health, job execution, labeling quality, data lineage, and GT release readiness.
- Optimize distributed processing and GPU inference workloads to improve throughput, resource utilization, cost efficiency, and release turnaround time.
- Drive technical decisions and cross-functional execution with perception, autolabeling modeling, data operations, and partner-facing teams to translate requirements into production solutions.
- Establish engineering best practices while leading complex technical initiatives.
What We Need to See
- BS, MS, or PhD in Computer Science, Software Engineering, Data Engineering, or a related field, or equivalent practical experience.
- 8+ years of relevant industry experience building production data infrastructure, MLOps platforms, or large-scale machine learning systems.
- Strong programming skills in Python and C++, with experience designing maintainable, well-tested software and troubleshooting complex production systems.
- A solid understanding of computer vision, machine learning, VLMs, and AI agent development, including evaluation and production integration.
- Experience building reliable data pipelines with data validation, versioning, lineage, and monitoring.
- Demonstrated ability to independently lead complex projects, make sound architectural decisions, and deliver measurable improvements in scalability, reliability, or performance.
- Strong communication and collaboration skills, with the ability to work effectively across global, multidisciplinary teams.
Ways to Stand Out from the Crowd
- Experience with Physical AI, autonomous driving, or robotics data platforms, particularly multimodal sensor processing and GT generation.
- Hands-on experience developing and evaluating AI agents for auto-labeling, quality assurance, or workflow automation.
- Experience with vision-language models, multimodal foundation models, or model-assisted annotation workflows.
- Experience delivering multi-region data platforms with access controls, data governance, and regional data-handling requirements.
- Fluent English communication and writing skills.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward‑thinking and hardworking people in the world working for us. If you’re passionate about AI, data, defining what the model can and cannot learn, and enabling world‑class AI models with high‑quality data, we want to hear from you!
How we rate this
Senior Physical AI Data Engineer at NVIDIA rates 97 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● 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. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data engineer jobs at this level
Pay
- Typical data engineer job
- $145k
This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs
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.
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: AI agents, ML Ops, and Computer vision. 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.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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