GPU Performance / Kernel Engineer

Opedia Technologies

GPU Performance / Kernel Engineer optimizing GPU workloads and performance for AI infrastructure. Working with a collaborative engineering team to enhance AI workload efficiency.

Posted 7/21/2026full-timeBellevue • Washington • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

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 profiling, debugging, and tuning performance-critical workloads in complex distributed computing environments.

Highest-signal resume keywords

GPU Kernel DevelopmentPerformance OptimizationCUDA ProgrammingGPU Architecture UnderstandingSystems Programming

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

GPU Kernel DevelopmentPerformance OptimizationProfilingDebuggingParallel ComputingAI Workload OptimizationPerformance MeasurementBenchmarking MethodologiesData-Plane Bottleneck EliminationLatency Reduction

Soft Skills

Problem SolvingIndependent OwnershipCollaboration

Tools & Technologies

CUDAROCmPerformance ToolsAI InfrastructureMachine Learning Frameworks

Industry Keywords

GPU PerformanceAI WorkloadsDistributed ComputingEngineering PracticesScalability

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 technologies such as 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.

Benefits

Comp & perks

Competitive base pay for Bellevue market
Certain roles are eligible for additional rewards, including merit increases, annual bonus, and stock. These awards are allocated based on individual performance
U.S. based employees have access to medical, dental, and vision insurance, a 401(k) plan and company match, employees also receive per calendar year, paid holidays.