Job description

We're now looking for a Sr. Inference Engineer, for GPU Kernel Optimization! What does it take to push every LLM inference operation to its performance ceiling? Our LLM Inference Performance Analysis and Optimization team builds the answer from the ground up. We develop silicon-measured kernel benchmarking infrastructure, model-level performance projection tooling, and agentic optimization systems that improve GPU kernels at the assembly layer. Our team works closely with compiler, kernel, hardware, and framework organizations across NVIDIA to surface bottlenecks and ship measurable gains. If driving GPU performance at the frontier of LLM inference sounds like your kind of challenge, we'd love to meet you! What you'll be doing: The role drives three interconnected systems, all aimed at accelerating NVIDIA's LLM inference stack. The first is GPU kernel microbenchmarking: measuring competing kernel implementations at real-silicon fidelity across the full configuration space that production LLM deployments demand. The second is end-to-end model performance analysis: connecting performance evidence to model-level serving economics, surfacing high-value optimization opportunities, and producing optimization policies for production inference deployments. The third is agentic kernel optimization: applying AI-driven analysis to diagnose performance gaps, explore optimization opportunities across the kernel ecosystem, and validate findings with rigorous silicon measurements. All three streams converge in close collaboration with compiler, hardware, kernel, and framework teams to deliver upstream improvements and production-grade performance gains. What we need to see • Master's or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience. • 6+ years of relevant industry experience. • Experience building or directing agentic AI systems — code generation, automated optimization, or multi-step reasoning workflows. • Strong Python and C++ skills with proven software engineering fundamentals. • Hands-on GPU profiling with CUPTI, NSYS, and NCU; proven track record to attribute bottlenecks across kernel execution, compiler decisions, and runtime scheduling. • Direct experience with LLM inference frameworks such as TRT-LLM, SGLang, or vLLM and clear understanding of how kernel selection drives model-level throughput and latency. • Working knowledge of GPU kernel optimization — CUDA, CUTLASS, Triton, or equivalent — and the ability to read PTX or SASS output. Ways to stand out from the crowd • Deep knowledge of SASS/PTX-level kernel analysis, compiler middle-end optimization, or GPU code generation pipelines (LLVM, MLIR, ptxas, or similar). • Track record shipping agentic systems end-to-end — tool invent, multi-agent orchestration, and silicon-verified validation — within a performance engineering or kernel optimization context. • Active contributions to open-source LLM inference or GPU kernel libraries (FlashInfer, Triton, CUTLASS, or similar). Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/ Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until July 31, 2026. 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.

Requirements

Must-haves

What we need to see
Master's or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience.
6+ years of relevant industry experience.
Experience building or directing agentic AI systems — code generation, automated optimization, or multi-step reasoning workflows.
Strong Python and C++ skills with proven software engineering fundamentals.
Hands-on GPU profiling with CUPTI, NSYS, and NCU; proven track record to attribute bottlenecks across kernel execution, compiler decisions, and runtime scheduling.
Direct experience with LLM inference frameworks such as TRT-LLM, SGLang, or vLLM and clear understanding of how kernel selection drives model-level throughput and latency.
Working knowledge of GPU kernel optimization — CUDA, CUTLASS, Triton, or equivalent — and the ability to read PTX or SASS output.
Ways to stand out from the crowd
Deep knowledge of SASS/PTX-level kernel analysis, compiler middle-end optimization, or GPU code generation pipelines (LLVM, MLIR, ptxas, or similar).
Track record shipping agentic systems end-to-end — tool invent, multi-agent orchestration, and silicon-verified validation — within a performance engineering or kernel optimization context.
Active contributions to open-source LLM inference or GPU kernel libraries (FlashInfer, Triton, CUTLASS, or similar).
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until July 31, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

llmaipython

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Effort: medium