Staff GPU Performance / Kernel Engineer

Location: Hybrid | Bellevue, WA (downtown)
multiple roles available

Optimize the Performance Layer Powering Next-Generation AI Infrastructure

About the Opportunity

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 Engineer 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.

  

The Opportunity

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.

  

What You'll Do

• 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.