Hardware Systems Engineer - AI Hardware

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.

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.