Senior GPU Kernel Performance Engineer

Designworks Talent; Bellevue (WA); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) with company match

Paid holidays

Job summary

Designworks Talent in Bellevue, WA is seeking a GPU Performance / Kernel Engineer to optimize the data plane powering large-scale AI workloads. You will tune kernels, reduce latency, and maximize throughput across training and inference environments, collaborating across AI infrastructure, ML, and platform teams.

The role focuses on identifying performance bottlenecks, profiling GPU kernels, and driving efficiency in a fast-moving engineering environment.

Qualifications

• Experience with GPU kernel development and performance optimization (CUDA/ROCm).
• Strong understanding of GPU architecture and memory hierarchy.
• Experience profiling and debugging performance issues in AI/distributed environments.

Responsibilities

• Profile, analyze, and optimize GPU kernels to improve latency and throughput.
• Identify and eliminate data-plane bottlenecks impacting GPU performance across large-scale workloads.
• Tune performance-critical workloads across training and inference environments.
• Collaborate with AI infrastructure, ML, and platform engineering teams to optimize system behavior.
• Develop benchmarking methodologies and performance measurement practices across GPU infrastructure.
• Evaluate emerging GPU technologies and optimization techniques for evolving hardware.
• Contribute to engineering practices that improve GPU efficiency, scalability, and reliability across the fleet.

Skills

CUDA

Kernel optimization

GPU architecture

Profiling tools

Distributed AI workloads

C/C++ systems programming

Nsight/ROCm tools

Tools

Nsight Systems

Nsight Compute

ROCm profiling tools

Job description

Designworks Talent in Bellevue, WA is seeking a GPU Performance / Kernel Engineer to optimize the data plane powering large-scale AI workloads. You will tune kernels, reduce latency, and maximize throughput across training and inference environments, collaborating across AI infrastructure, ML, and platform teams.

The role focuses on identifying performance bottlenecks, profiling GPU kernels, and driving efficiency in a fast-moving engineering environment.