Senior Performance Architect, Nemotron

Posted Yesterday
Be an Early Applicant
3 Locations
In-Office
152K-288K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develop high-fidelity performance models for AI systems, prioritize features for software and hardware roadmaps, and evaluate the performance of GenAI workflows.
Summary Generated by Built In

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 architectures and the Nemotron Super 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.

What we need to see:

  • A minimum qualification of a Master's degree (or equivalent experience) in Computer Science, Electrical Engineering or related fields.

  • Strong background in computer architecture, roofline modeling, queuing theory and statistical performance analysis techniques.

  • Solid understanding of ML fundamentals, model parallelism and 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

  • A Growth mindset and pragmatic “measure, iterate, deliver” approach.

Ways to Stand Out from the Crowd

  • 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)

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May 23, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Skills Required

  • Master's degree in Computer Science, Electrical Engineering or related fields
  • 3+ years of hands-on experience in system evaluation of AI/ML workloads
  • Strong background in computer architecture and performance analysis techniques
  • Solid understanding of ML fundamentals and model parallelism
  • Proficiency in Python and optionally C++
  • Experience with deep learning frameworks like PyTorch

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

NVIDIA Insights

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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