Cloudflare Hardware Systems Engineer at Cloudflare focusing on AI hardware infrastructure including GPUs and servers. Responsible for designing, qualifying, and maintaining Cloudflare’s worldwide fleet of GPU servers.
Responsibilities
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.
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
Qualification
Experience with RDMA networkingUnderstanding of AI workload patternsExperience of kernel development
Required
A self-starter who identifies what needs to be done and executes without waiting to be asked
Genuinely curious — you read architecture whitepapers, follow chip announcements, and have opinions on why one memory subsystem beats another
Equally comfortable racking servers in a lab and writing a detailed technical requirements document
Able to make a defensible technical decision with incomplete information, and communicate the reasoning clearly
Preferred
Experience with major AI server ODMs/OEMs and their qualification and customization processes
Exposure to hyperscaler or cloud infrastructure design — understanding how GPU capacity is provisioned, allocated, and operated at scale
Experience with RDMA networking, RoCEv2 congestion control, or InfiniBand fabric management
Understanding of AI workload patterns: LLM training vs. inference, model/tensor/pipeline parallelism, and their memory and bandwidth implications
Familiarity with open compute hardware programs or OCP specifications
Experience of performance optimization based on hardware / software codesign
Experience of kernel development, compiler optimization, or driver development.
Experience of observability and monitoring tools such as Prometheus and Grafana, as well as the ability to observe trends over time