GPU Performance / Kernel Engineer

Designworks Talent•Bellevue, WA

2d•Hybrid

About The Position

A well-funded, rapidly growing AI infrastructure company is building a next-generation cloud platform designed to power the full lifecycle of artificial intelligence. The organization is developing a comprehensive AI infrastructure, platform, and services portfolio that supports the full spectrum of AI workloads—including large-scale compute, model training, fine-tuning, inference, and emerging agentic AI applications. Backed by significant long-term investment, the company combines the speed, ownership, and innovation of a startup with the stability and resources of an established parent organization. Engineering teams are intentionally lean, highly collaborative, and AI-native, leveraging modern tooling and automation to build infrastructure capable of supporting the industry's most demanding AI workloads. We're seeking GPU Performance / Kernel Engineers to optimize the data plane powering large-scale AI workloads. This role focuses on improving GPU utilization, reducing latency, and maximizing throughput across training and inference environments by tuning kernels, identifying performance bottlenecks, and driving efficiency across the GPU fleet. This is a high-impact engineering role focused on extracting maximum performance from large-scale GPU infrastructure. You'll work at the intersection of GPU architecture, AI workloads, systems performance, and low-level optimization. As part of a highly technical infrastructure team, you'll analyze workload behavior, optimize performance-critical code paths, and develop the techniques and tooling required to operate AI systems efficiently at scale. This opportunity is ideal for engineers who enjoy deep technical challenges involving GPU computing, kernel optimization, distributed AI workloads, and hardware/software performance.

Requirements

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.

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 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.