16 hours ago
Shanghai, ChinaStaff+
Responsibilities
- Define architecture, technical direction, roadmaps, and platform capabilities for major areas of benchmark infrastructure.
- Build reusable software, services, and workflows for benchmark definition, execution, result collection, validation, and reporting across local, cluster, and cloud-native environments.
- Improve the reliability, scalability, observability, maintainability, and reproducibility of large benchmark campaigns.
- Conduct AI benchmarking, analysis, and benchmark design for large language models, agentic AI, HPC, and other workloads.
- Lead cross-team delivery by aligning partner teams, managing technical risk, resolving ambiguity, and communicating with stakeholders.
- Raise engineering quality through architecture and code reviews, technical standards, mentoring, and evaluation of emerging technologies.
- Contribute to internal and open-source projects and increase their adoption.
Requirements
- M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or Computer Engineering from a leading university, or equivalent experience.
- 10+ years of hands-on experience developing system software, distributed systems, or AI systems.
- Strong Python and software engineering expertise, with experience building and operating production-quality tools, services, or automation frameworks.
- Technical leadership experience on sophisticated, multifunctional projects, including architecture definition, technical risk management, and delivery through adoption.
- Experience with quantitative analysis of AI hardware and systems, including performance, architecture evaluation, and power efficiency.
- Experience operating distributed or cloud-native systems at scale, including performance profiling, capacity analysis, or resource scheduling.
- Strong communication, influence, presentation, and cross-functional coordination skills across countries and time zones.
- Experience mentoring engineers and improving engineering quality and technical decision-making.
- Preferred experience with modern compute hardware, AI workload performance characteristics, custom agent harnesses, and AI training or inference frameworks such as vLLM, SGLang, TensorRT-LLM, PyTorch, or JAX.
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.