About Us

At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company.

At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in.

Available Locations: Austin, Texas | Lisbon, Portugal

About The Department

Cloudflare’s Infrastructure group is responsible for building our global network. Our Hardware Engineering team helps research, develop, test, and deploy new equipment enabling 20% of the world’s internet traffic to be served smoothly. Deployed across 330 cities in 120+ countries, the hardware we select helps improve the security, reliability, and performance of the Internet.

About The Role

We need to make thoughtful infrastructure choices affecting a significant portion of the Internet. Hardware we work with includes GPUs, servers and components, as well as PDUs and network hardware. As a Hardware Systems Engineer, you will work with colleagues on the Engineering teams, Product and Performance teams, and Hardware Sourcing teams to design, qualify and maintain Cloudflare’s worldwide fleet of GPU servers.

What You'll Do

Contribute to the technical requirements and end-to-end delivery of AI infrastructure — compute, networking, storage, power, and cooling — not just the accelerator
Evaluate, select, and qualify AI hardware platforms (GPUs, custom ASICs, SmartNICs, storage) based on real workload requirements and first-principles analysis
Design and validate rack-scale GPU systems: multi-node topologies, high-speed interconnects (NVLink, InfiniBand, RoCE), power distribution, and thermal management
Run hands-on qualification: benchmarking, stress testing, failure mode analysis, and vendor RCA — you close the loop yourself
Assess and qualify ODM/OEM server platforms; engage directly with vendors and work through the full technical qualification cycle
Translate AI product and workload requirements into precise infrastructure specifications
Identify gaps in the existing infrastructure stack and drive solutions across hardware, firmware, software, and operations
Stay ahead of the AI hardware landscape — new silicon, new interconnects, new form factors — and bring informed, opinionated recommendations
Build tooling and automation to support qualification workflows, performance analysis, and ongoing operational visibility
Communicate your results and updates through blog posts and internal talks

Examples Of Desirable Skills, Knowledge And Experience

Bachelor’s, Master’s, or PhD degree in Computer Engineering, Electrical Engineering, or Computer Science
Experience in AI infrastructure, systems engineering, or hardware platform engineering
First-principles understanding of AI accelerator architecture: compute pipelines, memory hierarchy (HBM, GDDR), interconnect topology, and/or how workload characteristics map to hardware capabilities