Senior Physical AI Data Engineer

NVIDIA's 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.

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.