Staff GPU Performance, Kernel Engineer

Designworks Talent LLC

. Profile, analyze, and optimize GPU kernels to improve latency, throughput, and overall utilization .

Posted 10/2/2026full-timeBellevue • Washington • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in GPU kernel development and performance optimization, with a strong focus on improving latency, throughput, and overall utilization in AI workloads. Proficient in collaborating with cross-functional teams to enhance GPU efficiency and scalability across diverse environments.

Highest-signal resume keywords

GPU Kernel DevelopmentPerformance Optimization Using CUDAUnderstanding of GPU ArchitectureProfiling and Debugging Performance IssuesExperience with NVIDIA and AMD GPU Architectures

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

CUDAROCmGPU Performance OptimizationParallel ComputingSystems ProgrammingPerformance EngineeringBenchmarking MethodologiesData Path OptimizationAI Workload OptimizationLow-Level Systems Performance

Soft Skills

Problem-SolvingCollaborationOwnership of Complex Problems

Tools & Technologies

Nsight SystemsNsight ComputeROCm Profiling Tools

Industry Keywords

AI InfrastructureLarge-Scale AI TrainingInference PlatformsHPC EnvironmentsCloud GPU Infrastructure

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Profile, analyze, and optimize GPU kernels to improve latency, throughput, and overall utilization
Identify and eliminate data-plane bottlenecks affecting GPU performance across large-scale AI workloads
Tune performance-critical workloads across training and inference environments
Collaborate with AI infrastructure, machine learning, and platform engineering teams to understand workload characteristics and optimize system behavior
Develop benchmarking methodologies and performance measurement practices across GPU infrastructure
Evaluate emerging GPU technologies, performance tools, and optimization techniques as hardware platforms evolve
Contribute to engineering practices that improve GPU efficiency, scalability, and reliability across the fleet
Optimize the performance layer powering next-generation AI infrastructure

Requirements

What you’ll need

Strong experience with GPU kernel development and performance optimization using CUDA, ROCm, or comparable GPU programming frameworks
Demonstrated experience improving GPU utilization, reducing latency, or increasing throughput for production AI workloads
Strong understanding of GPU architecture, memory hierarchy, parallel computing, and the data path from application layer to hardware execution
Experience profiling and debugging performance issues in complex AI or distributed computing environments
Ability to independently own technically complex problems and drive solutions in a fast-moving engineering environment
Strong systems programming and performance engineering mindset
Preferred: experience optimizing workloads across NVIDIA and AMD GPU architectures
Preferred: experience with GPU compiler technologies, runtime optimization, or low-level systems performance
Preferred: contributions to open-source GPU performance projects, compiler tooling, or AI systems optimization
Preferred: background in large-scale AI training, inference platforms, HPC environments, or cloud GPU infrastructure
Preferred: familiarity with Nsight Systems, Nsight Compute, ROCm profiling tools, or similar technologies
U.S. work authorization required
Visa sponsorship is not currently available
Minimum three days per week in the office once the permanent office is established

Benefits

Comp & perks

Certain roles are eligible for merit increases, annual bonus, and long term incentives based on individual performance
Medical, dental, and vision insurance for U.S.-based employees
401(k) plan and company match
Paid holidays per calendar year
Hybrid work arrangement