Nvidia

AI

SeniorPerformanceArchitect

$152–288k Santa Clara, California, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Performance Architect at Nvidia. Skills: Performance Modeling, AI/ML Workloads, Deep Learning, System Evaluation, Model-Hardware Co-design. Develop high-fidelity analytical performance models to prototype emerging algorithmic techniques & hardware optimizations to drive model-hardware co-design Nemotron family of models. Prioritize features to guide future software and hardware roadmap based on detailed performance modeling and analysis”

What You'll Achieve.

ensure that future models achieve Pareto-optimal trade-offs across accuracy, throughput, and interactivity on target platforms; guide decisions that determine how efficiently intelligence scales in production

Industry & Context.

AI
Problems you'll solve

performance modeling; analysis; forward projections; architectural choices; deployment efficiency; Pareto-optimal trade-offs; accuracy; throughput; interactivity; model architecture; system efficiency; Generative AI evolution; performance modeling and analysis; end-to-end performance impact; Speculative Decoding; Agentic Pipelines; Inference-time compute scaling; RL; datacenter needs; DL research; hardware roadmap; software roadmap; simulator design; data analysis; system evaluation of AI/ML workloads; performance analysis; modeling; optimizations for AI; defining metrics; designing experiments; visualizing large performance datasets; identify resource bottlenecks

What They're Looking For.

Must Have

Master's degree (or equivalent experience) in Computer Science, Electrical Engineering or related fields, background in computer architecture, roofline modeling, queuing theory, statistical performance analysis techniques, Solid understanding of ML fundamentals, model parallelism, inference serving techniques, Proficiency in Python (and optionally C++) for simulator design and data analysis, 3+ years of hands-on experience in system evaluation of AI/ML workloads or performance analysis, modeling and optimizations for AI, Comfortable defining metrics, designing experiments and visualizing large performance datasets to identify resource bottlenecks, Experience with deep learning frameworks like PyTorch, TRT-LLM, VLLM, SGLang

Nice to Have

Proven track record of working in multi-functional teams, spanning algorithms, software and hardware architecture, Ability to distill complex analyses into clear recommendations for both technical and non-technical collaborators, Experience with GPU computing (CUDA)

What You'll Do.

Develop high-fidelity analytical performance models to prototype emerging algorithmic techniques & hardware optimizations to drive model-hardware co-design Nemotron family of models

Prioritize features to guide future software and hardware roadmap based on detailed performance modeling and analysis

Model end-to-end performance impact of emerging GenAI workflows - such as Speculative Decoding

Inference-time compute scaling

RL etc. – to understand future datacenter needs

How You'll Work.

Team & Collaboration

partnering across research, framework development, compiler, and hardware teams to guide decisions; collaborate with diverse teams, including DL researchers, hardware architects, and software engineers; working in multi-functional teams, spanning algorithms, software and hardware architecture; Ability to distill complex analyses into clear recommendations for both technical and non-technical collaborators

Communication Scope

Ability to distill complex analyses into clear recommendations for both technical and non-technical collaborators

Full Job Description

We are now looking for a Senior Performance Architect for Nemotron! At NVIDIA, we are redefining the future of AI systems through deep model–system–hardware co-design. We are looking for a forward-thinking Nemotron Performance Architect to shape the next generation of Nemotron models through performance modeling, analysis, and forward projections. In this role, you will predict before we build - developing high-fidelity models to evaluate how architectural choices translate into real-world deployment efficiency. You will ensure that future models achieve Pareto-optimal trade-offs across accuracy, throughput, and interactivity on target platforms. Recent efforts such as [LatentMoE](https://research.nvidia.com/labs/nemotron/LatentMoE/) architectures and the [Nemotron Super](https://developer.nvidia.com/blog/introducing-nemotron-3-super-an-open-hybrid-mamba-transformer-moe-for-agentic-reasoning/) model exemplify the kind of performance-driven co-design you will help advance—where modeling insights directly shape model architecture and system efficiency at scale. This role sits at the center of Generative AI evolution, partnering across research, framework development, compiler, and hardware teams to guide decisions that determine how efficiently intelligence scales in production. **What You’ll Be Doing:** * Develop high-fidelity analytical performance models to prototype emerging algorithmic techniques & hardware optimizations to drive model-hardware co-design Nemotron family of models. * Prioritize features to guide future software and hardware roadmap based on detailed performance modeling and analysis * Model end-to-end performance impact of emerging GenAI workflows - such as Speculative Decoding, Agentic Pipelines, Inference-time compute scaling, RL etc. – to understand future datacenter needs * This position requires you to keep up with the latest DL research and collaborate with diverse teams, including DL researchers, hardware architects, and software engineers.

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