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Senior Physical AI Data Engineer

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Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
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What you’ll work on

Full posting
  • 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.

From the employer’s posting
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
Education & alternatives
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.
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Posting record

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Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Nov 7, 2026today
Recorded sightings
1

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How this job compares

Time since we first saw it

Day 0

Early. Similar jobs usually come down between day 6 and day 54.

At Nvidia, jobs usually come down by about day 41.

From 11,508 similar postings that came down, and 84 of Nvidia’s. Coming down is not always a hire.

Nvidia is hiring

54 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Data Engineer hiring is picking up
  • Takes jobs down at a usual pace
Hiring record

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  • Python
  • C++
Where the posting names them
- 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.

Your next step

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About Nvidia

People
About 42,000 in 2026, up 17% from the year before.Annual report (SEC Form 10-K)
Company
Listed as NVDA.
H-1B visas
978 approved for new hires in fiscal 2025.US Citizenship and Immigration Services

Demand for Data Engineer roles

New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.

Weekly new Data Engineer postings at the same employers, from the week of August 17 to the week of October 26. The fewest in a week was 839, the most 1,314, and the latest week had 1,079.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 5 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under Data Engineer, by the work the posting describes.

Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.

Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.

An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.

US government figures for database architects

Median yearly pay, May 2025
$139,500
Expected change, 2025 to 2035
9.4% more jobsagainst 3.5% for all jobs

The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

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!

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China, Shanghai

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