HPC/GPU Performance, Kernel Engineer – Member of Technical Staff

Designworks Talent LLC

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

Posted 11/10/2026full-timeBellevue • Washington • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in GPU Kernel Development and Performance Optimization, with a strong focus on CUDA and ROCm frameworks. Proven ability to enhance GPU utilization and throughput for AI workloads while collaborating with cross-functional teams to optimize system performance.

Highest-signal resume keywords

GPU Kernel DevelopmentPerformance Optimization Using CUDAGPU Architecture UnderstandingProfiling And Debugging Performance IssuesExperience With NVIDIA And AMD Architectures

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

GPU Kernel DevelopmentPerformance OptimizationCUDAROCmParallel ComputingSystems ProgrammingPerformance EngineeringGPU Compiler TechnologiesRuntime OptimizationAI Workload Optimization

Soft Skills

Problem-SolvingIndependent OwnershipCollaboration

Tools & Technologies

Nsight SystemsNsight ComputeROCm Profiling Tools

Industry Keywords

AI WorkloadsHPC EnvironmentsCloud GPU InfrastructureOpen-Source GPU Performance Projects

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 impacting GPU performance across large-scale AI workloads
Tune performance-critical workloads across training and inference environments
Work closely 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

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
Experience optimizing workloads across multiple GPU platforms, including NVIDIA and AMD architectures
Experience with GPU compiler technologies, runtime optimization, or low-level systems performance
Contributions to open-source GPU performance projects, compiler tooling, or AI systems optimization
Background working with large-scale AI training, inference platforms, HPC environments, or cloud GPU infrastructure
Familiarity with GPU profiling and optimization tools such as Nsight Systems, Nsight Compute, ROCm profiling tools, or similar technologies
U.S. work authorization is required

Benefits

Comp & perks

U.S. based employees have access to medical, dental, and vision insurance
401(k) plan and company match
Paid holidays per calendar year
Certain roles are eligible for merit increases
Certain roles are eligible for annual bonus
Certain roles are eligible for long term incentives