About the job
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!
Responsibilities
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.
Qualifications
Minimum
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
Preferred
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)
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.