Senior High Performance AI Engineer

NVIDIA•Austin, CA

About The Position

NVIDIA is hiring a Senior High Performance AI Engineer to build groundbreaking multi-agent systems for the CUDA ecosystem. The team develops agentic runtimes and compiler-integrated orchestration that work with NVIDIA’s software stack to accelerate modern agent workloads powered by foundational models. The engineer will develop agent abstractions, GPU-centric runtimes, and compiler- or runtime-driven solutions for agent planning, tool use, code generation, and other AI workloads, collaborating with NVIDIA software and hardware teams to bring new developments into NVIDIA products.

Requirements

Bachelor’s degree in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
At least 8 years of industry or academic experience with AI systems development.
Exposure to building foundational models, agents, or orchestration frameworks.
Hands-on experience with deep learning frameworks and modern inference stacks.
Strong C/C++ and Python programming skills.
Solid software engineering fundamentals.
Experience with GPU programming and performance optimization using CUDA or an equivalent.

Nice To Haves

MS or PhD.
Track record building or evaluating deep learning models, coding agents, and developer tooling.
Demonstrated ability to optimize and deploy high-performance models, including on resource-constrained platforms.
Deep expertise in GPU performance optimization, demonstrated by benchmark wins or published results.
Publications or open-source leadership in deep learning, multi-agent systems, reinforcement learning, or AI systems.
Contributions to widely used repositories or standards.

Responsibilities

Design, build, and optimize agentic AI systems for the CUDA ecosystem.
Co-design agentic system solutions with software, hardware, and algorithm teams; influence and adopt new capabilities as they become available.
Develop reproducible, high-fidelity evaluation frameworks covering performance, quality, and developer productivity.
Collaborate across the AI stack, including hardware, compilers and toolchains, kernels and libraries, frameworks, distributed training, inference and serving, and model and agent teams.