Senior Inference Engineer, GPU Kernel Optimization

About the role

The role drives three interconnected systems, all aimed at accelerating NVIDIA's LLM inference stack. These systems include GPU kernel microbenchmarking, end-to-end model performance analysis, and agentic kernel optimization.

Responsibilities

• Measure competing kernel implementations at real-silicon fidelity across the full configuration space demanded by production LLM deployments.
• Connect performance evidence to model-level serving economics, identify high-value optimization opportunities, and produce optimization policies for production inference deployments.
• Apply AI-driven analysis to diagnose performance gaps, explore optimization opportunities across the kernel ecosystem, and validate findings with rigorous silicon measurements.
• Collaborate closely with compiler, kernel, hardware, and framework teams to deliver upstream improvements and production-grade performance gains.

Requirements

• 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.
• Workings knowledge of GPU kernel optimization — CUDA, CUTLASS, Triton, or equivalent — and the ability to read PTX or SASS output.

Qualifications

• 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).

Skills

• Expertise in LLM inference frameworks such as TRT-LLM, SGLang, or vLLM.
• Experience with GPU profiling tools like CUPTI, NSYS, and NCU.
• Knowledge of CUDA, CUTLASS, Triton, or equivalent GPU kernel optimization techniques.
• Ability to analyze and optimize GPU kernels at the assembly level.

Benefits

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 here. 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 - $287,500. You will also be eligible for equity and benefits.

Pay

$184,000 - $287,500

Schedule

Full-time