NVIDIA’s AI and GPU software is at the forefront of computing fueling breakthroughs across deep learning, LLMs, and intelligent applications. Our team is building solutions for rapid development and deployment of GPU kernels for AI systems. We take the latest AI models, rigorously analyze them, develop and deploy high-performance GPU kernels that define model performance and integrate the derived techniques and methodologies into the tools that automate this process.
This role is a unique opportunity to shape the next generation of AI performance and efficiency. You will work hands-on with emerging AI models, collaborating across compiler, AI inference, and model performance teams. The focus is on building programming solutions that can be applied to concrete AI inference use cases to deliver real-world performance and development efficiency wins.
What you will be doing:
Analyze state-of-the-art AI models, identifying key performance bottlenecks and opportunities at the kernel level.
Develop, optimize, and evaluate both hand-tuned and compiler-generated kernels for inference workloads, balancing speed and flexibility.
Design and build high-level DSLs and innovative compiler infrastructure to increase kernel developer productivity while achieving near peak performance.
Collaborate with model AI inference and compiler teams to iterate on kernel fusion, auto tuning, and sophisticated GPU programming techniques.
Benchmark performance across real workloads, diagnose root causes, and rapidly deploy optimizations that maximize hardware utilization on NVIDIA platforms.
What we need to see:
Bachelor’s, master’s or PhD degree in Computer Science, Computer Engineering or related field, or equivalent experience.
At least 3+ years Strong C++ and/or Python programming skills for system and performance engineering.
Understanding of GPU architecture and proficiency in CUDA programming.
Intellectual curiosity and interest to solve exciting problems and deliver practical results in production environments.
Ways to stand out from the crowd:
Experience designing, developing and optimizing high-efficiency GPU kernels for modern AI workloads.
Experience building compilers, domain-specific languages, or automatic optimization systems
Familiarity with popular compiler, GPU programming and AI frameworks such as MLIR, LLVM, PyTorch, XLA, Triton or Cutlass.
Experience with AI/ML inference workloads and model performance analysis.
Strong communication skills and ability to collaborate in a cross-team environment.
You will also be eligible for equity and benefits.
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.
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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.
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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.
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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.
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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.”
