5 days ago
Shanghai, ChinaSenior
Responsibilities
- Develop and productionize AI-powered auto-labeling workflows using computer vision models, vision-language models, and AI agents, with 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 for pipeline health, job execution, labeling quality, data lineage, and ground-truth release readiness.
- Optimize distributed processing and GPU inference workloads for 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.
- Lead complex technical initiatives and establish engineering best practices.
Requirements
- 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.
- Understanding of computer vision, machine learning, vision-language models, and AI agent development, including evaluation and production integration.
- Experience building reliable data pipelines with validation, versioning, lineage, and monitoring.
- Ability to independently lead complex projects, make architectural decisions, and deliver improvements in scalability, reliability, or performance.
- Strong communication and collaboration skills across global, multidisciplinary teams.
- Preferred experience with physical AI, autonomous driving, robotics data platforms, multimodal sensor processing, ground-truth generation, AI-agent evaluation, vision-language or multimodal foundation models, model-assisted annotation, and multi-region data platforms with access controls and data governance.
- Fluent English communication and writing skills.
Categories
Data EngineeringML Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.