HPC/GPU Performance / Kernel Engineer - Member of Technical Staff

Designworks Talent • Bellevue, WA

1d • Hybrid

About The Position

The HPC/GPU Performance / Kernel Engineer is a high-impact engineering role optimizing the data plane that powers large-scale AI workloads. The role focuses on improving GPU utilization, reducing latency, and maximizing throughput across training and inference environments by tuning kernels, finding performance bottlenecks, and improving efficiency across the GPU fleet.

The engineer will work at the intersection of GPU architecture, AI workloads, systems performance, and low-level optimization. As part of a technical infrastructure team, they will analyze workload behavior, optimize performance-critical code paths, and develop techniques and tooling to operate AI systems efficiently at scale. The role also involves collaboration with AI infrastructure, machine learning, and platform engineering teams, and contributing to GPU efficiency, scalability, and reliability across the fleet.

This is a hybrid role based in the Bellevue, Washington area, with approximately three days per week in the office. Candidates elsewhere in the United States who are open to relocation are encouraged to apply. U.S. work authorization is required; visa sponsorship is not currently available.

Requirements

• 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.
• U.S. work authorization.

Nice To Haves

• 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.

Responsibilities

• 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.
• Work 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.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• 401(k) plan with company match
• Paid holidays
• Certain roles are eligible for merit increases, annual bonus, and long-term incentives based on individual performance.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed