Senior Data Infrastructure Engineer, AI Performance
NVIDIAJob Title
Senior Data Infrastructure Engineer, AI Performance
Role Summary
Build and scale the infrastructure that supports large-scale architecture simulation and performance studies for AI and GPU platforms. The role sits at the intersection of distributed systems, performance engineering, data platforms, and architecture and partners with architects, researchers, and software teams.
Experience Level
Senior — the posting expects experience; the team requests at least 3+ years building production infrastructure, distributed systems, or data platforms.
Responsibilities
Design, implement, and operate the platforms that enable large-scale, reproducible simulation studies and self-service analysis for GPU/AI architecture teams.
- Build scalable, reliable infrastructure for running large simulation studies on on-prem clusters and cloud, improving throughput and resource efficiency.
- Design and maintain a unified storage and data platform for simulation configurations, execution state, results, and provenance.
- Develop self-service analytics and visualization tools to explore results and compare performance, power, and design trade-offs.
- Collaborate with GPU architects, performance engineers, AI researchers, and software teams to translate workloads into platform capabilities.
- Contribute to the technical roadmap and engineering practices for next-generation architecture simulation platforms.
Requirements
Key qualifications and skills required for successful performance in the role.
- 3+ years experience building production infrastructure, distributed systems, or data platforms.
- Strong software engineering and system-design skills in one or more programming languages; solid understanding of scalability, reliability, data consistency, and operational trade-offs.
- Experience working through ambiguous problems, simplifying fragmented systems, and collaborating across engineering and research teams.
Nice-to-have
- Expertise in distributed systems, storage/query engines, compute orchestration, or large-scale data platforms.
- Experience improving performance, reliability, or efficiency of compute- or data-intensive systems.
- Knowledge of LLM inference optimization and performance characteristics of conversational/agentic AI workloads.
- Familiarity with architecture simulation, performance modeling, high-performance computing, or GPU computing.
- Proven technical ownership via industry work, research, or open-source contributions.
Education Requirements
BS or higher degree in a relevant technical field (Computer Science, Electrical Engineering, Computer Engineering, Mathematics, or similar).
About the Company
Company: NVIDIA
Headquarters: Santa Clara, California, USA
NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.
