Senior AI Engineer, High Performance AI

About the role

NVIDIA is building the next era of computing by harnessing the unlimited potential of AI. As a High Performance AI Engineer, you will design and implement groundbreaking multi-agent systems within the CUDA ecosystem. You will co-develop agentic runtimes and compiler-integrated orchestration that integrate with NVIDIA’s software stack to accelerate modern agent workloads powered by foundational models. This role involves creating new agent abstractions, GPU-centric runtimes, and system solutions that optimize agent planning, tool-use, code generation, and other high-impact AI workloads. You will collaborate closely with internal software and hardware teams to drive the latest innovations into NVIDIA products.

Responsibilities

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

Requirements

• Bachelor’s degree in Computer Science, Electrical Engineering, or related field (or equivalent experience); MS or PhD preferred.
• 3+ 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 (CUDA or equivalent).

Ways to stand out

• Strong experience in building/evaluating deep learning models, coding agents, and developer tooling.
• Demonstrated ability to optimize and deploy high-performance models, including on resource-constrained platforms.
• Demonstrated ability in GPU performance optimizations, evidenced 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 repos or standards.

Pay

Base salary range: $152,000 – $241,500 USD, determined by location, experience, and internal equity benchmarks. Eligible for equity and benefits.