Staff GPU Kernel Performance Engineer

Designworks Talent LLC

Bellevue (KY)

Hybrid

USD 180,000 - 240,000

Full time

6 days ago
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Benefits offered by this job

Medical, dental, and vision insurance
401(k) plan with company match
Paid holidays

Job summary

Designworks Talent LLC is seeking a Staff GPU Performance / Kernel Engineer in a hybrid Bellevue, WA setting to optimize GPU kernels and data-plane performance for AI workloads. You will collaborate with AI infrastructure and platform teams to understand workload characteristics and drive system improvements.

The role focuses on extracting maximum performance from large-scale GPU infrastructure, with opportunities to influence kernel development, tooling, and optimization practices in a

Qualifications

  • Experience optimizing GPU kernels for AI workloads.
  • Strong understanding of GPU memory hierarchy and parallelism.
  • Ability to identify and mitigate data-plane bottlenecks.

Responsibilities

  • Profile, analyze, and optimize GPU kernels to improve latency and throughput.
  • Identify and eliminate data-plane bottlenecks for large-scale AI workloads.
  • Tune performance-critical workloads across training and inference environments.
  • Collaborate with AI infrastructure, ML, and platform teams to optimize system behavior.
  • Develop benchmarking methodologies and performance measurement practices.

Skills

CUDA kernel development
GPU performance optimization
GPU architecture knowledge
Profiling & debugging
Systems programming
AI workloads experience

Tools

Nsight Systems
Nsight Compute
ROCm profiling tools
CUDA toolkit

Job description

Designworks Talent LLC is seeking a Staff GPU Performance / Kernel Engineer in a hybrid Bellevue, WA setting to optimize GPU kernels and data-plane performance for AI workloads. You will collaborate with AI infrastructure and platform teams to understand workload characteristics and drive system improvements.

The role focuses on extracting maximum performance from large-scale GPU infrastructure, with opportunities to influence kernel development, tooling, and optimization practices in a

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