Principal Engineer, Rack-Scale GPU Architecture

Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here. We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world.

We are looking for a Principal Engineer to own how rack-scale GPU systems are architected, orchestrated, and operated for inference at DigitalOcean.

DigitalOcean is the Inference Cloud. The unit of GPU capacity is changing underneath the entire industry: for a decade the server was the boundary, and now it is the rack. GB300 NVL72 and the Vera Rubin generation behind it present 72 or more GPUs inside a single coherent NVLink domain, which breaks most of the assumptions our scheduling, networking, failure-handling, and capacity models were built on. Getting this right determines whether we can serve trillion-parameter models with the economics our customers expect.

This role owns that transition. You will define the reference architecture for rack-scale inference at DigitalOcean—how NVLink domains are carved and allocated, how the scale-out RDMA fabric is designed and tuned around them, how Kubernetes is taught to schedule against topology it was never designed to understand, and what happens when one GPU in a 72-GPU coherent domain fails at 3am. You'll work across our data center, networking, and inference platform teams, and directly with NVIDIA on pre-silicon enablement and bringup.

• Owning the end-to-end reference architecture for rack-scale inference systems—GB300 NVL72 today, Vera Rubin–class NVL144 and successors next—from fabric design through to what a customer can actually schedule

• Defining how NVLink domains are partitioned, allocated, and isolated across tenants and workloads, including multi-node NVLink and IMEX domain provisioning, fabric manager configuration, and the blast radius each choice implies

• Architecting the scale-out RDMA fabric that connects racks—RoCEv2 or InfiniBand, rail-optimized topologies, congestion control, GPUDirect RDMA paths—and tuning NCCL to actually use it well

• Making Kubernetes topology-aware for these systems: DRA for GPU and NVLink-domain allocation, multi-node inference primitives such as LeaderWorkerSet, gang scheduling, Topology Manager and NUMA alignment, and the GPU and Network Operators that underpin all of it

• Driving multi-node disaggregated serving on this hardware in partnership with the inference platform team—how prefill and decode pools map onto NVLink domains and rail boundaries, and where the KV cache moves between them

• Owning failure semantics and serviceability at rack scale: health checking and DCGM-based diagnostics, drain and repair workflows, degraded-domain scheduling, and firmware and driver lifecycle across a fleet of racks

• Establishing the validation and burn-in methodology that qualifies a rack for production—collective bandwidth and latency characterization, NCCL and end-to-end inference benchmarks, and the acceptance gates a rack must pass

• Partnering with our data center engineering team on power, liquid cooling, and rack density constraints, and translating those into what is actually deployable and at what cost

• Working directly with NVIDIA and ODM partners on pre-silicon enablement, early hardware bringup, and roadmap feedback

• Setting technical direction, mentoring senior and staff engineers across teams, and representing DigitalOcean upstream, at conferences, and in customer architecture reviews

What You'll