We are looking for a passionate and energized engineer to accelerate the integration of APIs across the CUDA-X math library software stack into modern LLM and agentic AI workflows. Leading organizations globally are using GPU-powered data centers for AI, data analytics, and scientific simulations, driving advancements in fields like LLMs, computer vision, CAE, EDA, and autonomous vehicles. Our team develops the GPU-accelerated libraries and SDKs essential for these technologies.
In this role, you will be responsible for ensuring both human engineers using LLMs and AI agents alike build high-performance applications with ease through effective utilization of CUDA-X libraries. Do you have the rare blend of technical, developer experience and communication skills? If so, we would love to learn more about you!
What You'll Be Doing:
Drive the architecture and implementation of math libraries APIs and documentation structures designed to be LLM-first: introspectable, explainable, and easily synthesized by AI agents.
Work with internal and external stakeholders to deliver timely LLM-enhanced library releases.
Build metrics, tools and processes to measure impact and quality of LLM/agentic code generation.
Prototype tooling and solutions to help transform math libraries’ APIs into LLM-friendly representations across popular codegen tools.
What We Need To See:
PhD or MSc’s degree in Computational Science, Computer Science, Applied Math, or related science or engineering field of study is preferred (or equivalent experience)
3+ years of experience
Robust knowledge of LLMs, finetuning, RL, building RAGs, MCP, and building agenting tooling
Proven experience designing clear, composable APIs and writing high-quality, well-documented code for complex technical domains
Ability to prioritize multiple projects and work independently with minimal direction
Excellent collaboration, communication, and documentation habits
Ways To Stand Out From The Crowd:
Prior work in AI-assisted software engineering, code generation, or programming language design
Familiarity with CUDA-X math library APIs: cuBLAS, cuFFT, cuSOLVER, cuSPARSE, etc.
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing for science and engineering. 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, we are increasingly known as “the AI computing company.” We're looking to grow our company and build our teams with the smartest people in the world! Join us at the forefront of technological advancement. NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and talented people in the world working for us. If you're creative, autonomous and love a challenge, we want to hear from you!
#LI-Hybrid
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.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive 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
- 3+ years of professional experience
- PhD or MSc degree in Computational Science, Computer Science, Applied Mathematics, or a related science or engineering field, or equivalent experience
- Robust knowledge of LLMs, fine-tuning, reinforcement learning, RAG, MCP, and agent tooling
- Experience designing clear, composable APIs and writing high-quality, well-documented code for complex technical domains
- Ability to prioritize multiple projects and work independently with minimal direction
- Excellent collaboration, communication, and documentation skills
- Experience in AI-assisted software engineering, code generation, or programming language design
- Familiarity with CUDA-X math library APIs, including cuBLAS, cuFFT, cuSOLVER, and cuSPARSE
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.”
