NVIDIA is building the world's most advanced AI computing platforms, powering breakthroughs in generative AI, large language models, and scientific discovery. Our accelerated computing technologies enable researchers and engineers to push the boundaries of what's possible with artificial intelligence.
We are seeking an AI Networking Exploration Architect for our Networking Insights Group to bridge the gap between cutting-edge, hyper-scale AI workloads and the datacenter infrastructure that enables them. You will join a small, focused team of multidisciplinary engineers driving AI workload optimization through deep application understanding and end-to-end systems thinking. Your insights will directly shape NVIDIA's products across the full stack—from applications and software libraries to hardware architecture and physical design.
What You'll Be Doing:
Model the performance of complex AI workloads to identify bottlenecks and recommend system-level optimizations.
Translate state-of-the-art research into actionable infrastructure, software, and hardware features in partnership with architecture teams.
Rapidly master new AI domains (LLMs, generative models, multimodal systems) and distill key findings for product teams.
Incorporate your deep knowledge of AI applications into our hardware and software roadmaps.
Conduct independent research by formulating hypotheses about workload behavior and validating them through rigorous analysis.
Drive architectural innovation and network optimization by applying your domain expertise to exploratory analysis of real-world Deep Learning (DL) workloads.
What we need to see:
M.Sc. or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
+5 years of experience.
Strong ML/Data Science background with hands-on experience in LLMs or generative AI.
A systems-level mindset with the ability to estimate end-to-end requirements across the entire AI stack.
Proven ability to translate research and product requirements into clear software/hardware specifications.
Exceptional research skills: you can digest academic papers, self-learn new domains, and independently test hypotheses.
Advanced Python programming skills for performance modeling and data analysis.
Excellent communication skills, with the ability to present complex findings with clarity and conviction.
A pragmatic approach: you are detail-oriented but can prioritize effectively to focus on the most critical issues.
Ways to Stand Out from the Crowd:
Deep understanding of datacenter infrastructure, network topologies, and protocols.
Expertise in distributed training methods and their impact on infrastructure.
Knowledge of AI performance metrics and the impact of different deployment strategies.
Experience extrapolating academic research into tangible hardware architecture requirements.
A track record of leading complex, multidisciplinary research projects that result in production impact.
NVIDIA has some of the most forward-thinking and talented people in the world working for us. If you're an autonomous researcher passionate about connecting AI applications with the infrastructure that powers them, we want to hear from you.
Skills Required
- M.Sc. or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
- 5+ years of relevant experience.
- Strong ML/Data Science background with hands-on experience in LLMs or generative AI.
- Advanced Python programming skills for performance modeling and data analysis.
- Systems-level mindset to estimate end-to-end requirements across the AI stack.
- Proven ability to translate research and product requirements into software/hardware specifications.
- Exceptional research skills: digest academic papers, self-learn domains, independently test hypotheses.
- Excellent communication skills to present complex findings clearly.
- Pragmatic, detail-oriented with strong prioritization ability.
- Deep understanding of datacenter infrastructure, network topologies, and protocols.
- Expertise in distributed training methods and their infrastructure impact.
- Knowledge of AI performance metrics and deployment strategy impacts.
- Experience extrapolating academic research into hardware architecture requirements.
- Track record leading complex, multidisciplinary research projects with production impact.
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
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.”
.png)
