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About the Role

The LLM Inference Performance Analysis and Optimization team builds infrastructure for kernel benchmarking, performance projection tooling, and agentic optimization systems to improve GPU kernels at the assembly layer. This role drives three interconnected systems aimed at accelerating NVIDIA's LLM inference stack: GPU kernel microbenchmarking, end-to-end model performance analysis, and agentic kernel optimization. The goal is to surface bottlenecks and ship measurable gains in LLM inference performance.

Responsibilities

Measure competing kernel implementations at real-silicon fidelity across the full configuration space for production LLM deployments.
Connect performance evidence to model-level serving economics.
Surface high-value optimization opportunities.
Produce optimization policies for production inference deployments.
Apply AI-driven analysis to diagnose performance gaps.
Explore optimization opportunities across the kernel ecosystem.
Validate findings with rigorous silicon measurements.
Collaborate with compiler, hardware, kernel, 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 (e.g., TRT-LLM, SGLang, 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.

Skills

Python
C++
GPU profiling
CUPTI
NSYS
NCU
LLM inference frameworks
TRT-LLM
SGLang
vLLM
GPU kernel optimization
CUDA
CUTLASS
Triton
PTX
SASS
Agentic AI systems
Code generation
Automated optimization
Multi-step reasoning workflows
SASS/PTX-level kernel analysis
Compiler middle-end optimization
GPU code generation pipelines
LLVM
MLIR
ptxas
Open-source LLM inference
Open-source GPU kernel libraries
FlashInfer

Location

Remote

Work Type

Full-time

Experience Level

6+ years of relevant industry experience

Education Level

Master's or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience.

Salary/Compensations

184,000 USD - 287,500 USD

Benefits

Equity
Comprehensive benefits package

About the Company

Widely considered to be one of the technology world’s most desirable employers.
NVIDIA offers highly competitive salaries and a comprehensive benefits package.
NVIDIA uses AI tools in its recruiting processes.

Equal Opportunity

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer.
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.