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!

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!