Senior Data Infrastructure Engineer, AI Performance

Job Title

Senior Data Infrastructure Engineer, AI Performance

Role Summary

The AI Computing Architecture team builds models and simulators that guide GPU and system design. This role develops and scales the infrastructure—distributed execution, data, automation, and visualization—used to run large-scale architecture simulation studies.

Work sits at the intersection of distributed systems, performance engineering, data platforms, and architecture and involves close collaboration with architects, researchers, and software teams.

Experience Level

Senior-level. The posting requests 3+ years of experience building production infrastructure, distributed systems, or data platforms.

Responsibilities

Primary responsibilities focus on building and operating the platform that runs and analyzes large simulation studies.

• Design and implement scalable execution frameworks for on-premises and cloud compute clusters.
• Establish unified storage and data platforms for simulation configurations, execution state, results, and provenance.
• Develop self-service analytics and visualization tools to explore performance, power, and design trade-offs.
• Collaborate with GPU architects, performance engineers, AI researchers, and software teams to productize simulation capabilities.
• Define technical roadmap and engineering practices for next-generation architecture simulation platforms.
• Improve throughput, resource efficiency, reliability, and reproducibility of large-scale studies.

Requirements

Must-have technical skills and experience.

• 3+ years building production infrastructure, distributed systems, or large-scale 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.
• Ability to work through ambiguous problems and collaborate across engineering and research teams.
• Operational experience running services at scale (monitoring, debugging, performance tuning).

Nice-to-have:

• Deep expertise in distributed systems, storage and query engines, compute orchestration, or large-scale data platforms.
• Experience improving performance, reliability, or efficiency of compute- or data-intensive systems.
• Understanding of LLM inference optimization and performance characteristics of emerging AI workloads.
• Familiarity with architecture simulation, performance modeling, HPC, or GPU computing.
• Record of technical ownership and impact via industry work, research, or open-source contributions.

Education Requirements

BS or higher in Computer Science, Electrical Engineering, Computer Engineering, Mathematics, or a related technical field.

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.