High Performance AI Engineer

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +5 · Remote

NVIDIA is seeking a High Performance AI Engineer to build multi-agent systems for the CUDA ecosystem, focusing on agentic runtimes, compiler-integrated orchestration, and accelerating agent planning, tool-use, and code generation. The role involves designing, building, and optimizing these systems, collaborating with hardware and software teams, and developing evaluation frameworks.

What you'd actually do

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

Skills

Required

  • Bachelor’s degree in Computer Science, Electrical Engineering, or related field (or equivalent experience)
  • 3 years+ industry or academia 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)

Nice to have

  • MS or PhD preferred
  • Strong experience in building/evaluating deep learning models
  • coding agents
  • 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

What the JD emphasized

  • multi-agent systems
  • agentic runtimes
  • orchestration
  • foundational models
  • GPU acceleration
  • agent planning
  • tool-use
  • code generation
  • CUDA ecosystem
  • compiler-integrated orchestration
  • agentic AI systems
  • agentic system solutions
  • evaluation frameworks
  • AI stack
  • inference/serving

Other signals

  • multi-agent systems
  • agentic runtimes
  • orchestration
  • foundational models
  • GPU acceleration