Job description

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. What You'll Be Doing: 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 Add to DigitalOcean: 12+ years in large-scale compute, HPC, or GPU infrastructure engineering, including hands-on responsibility for systems in production Deep understanding of GPU interconnect and system topology—NVLink and NVSwitch, NVLink domains, PCIe, NUMA, and how each shapes what a workload can be placed where Strong RDMA networking background: RoCEv2 or InfiniBand at scale, GPUDirect RDMA, congestion control and lossless fabric tuning, and rail-optimized cluster topologies Practical NCCL expertise—not just running it, but debugging it: algorithm and protocol selection, topology detection, and diagnosing collectives that are slow for non-obvious reasons Substantial Kubernetes depth for accelerated workloads: device plugins and DRA, scheduling extensions, operators, and a clear-eyed view of where Kubernetes' model breaks down against rack-scale hardware Strong systems programming and automation skills in Go and Python, and comfort at the driver, firmware, and BMC layer when the problem lives there Experience operating fleets where hardware failure is routine, including the diagnostics, automation, and blast-radius thinking that makes that survivable Excellent written and verbal communication, and a track record of leading architecture across organizational boundaries—hardware, networking, platform, and product Bonus: Direct experience bringing up GB200 or GB300 NVL72 systems, or comparable rack-scale platforms, in a production environment Experience with multi-node inference for very large models—expert or pipeline parallelism spanning NVLink domains, and the placement constraints that follow Familiarity with liquid-cooled and high-density rack deployments, and the operational realities of servicing them Contributions to relevant open source: Kubernetes SIG-Node or scheduling, NVIDIA GPU or Network Operator, DRA drivers, Kueue, LeaderWorkerSet, or NCCL Experience running heterogeneous fleets spanning multiple accelerator vendors and generations under a single orchestration model Compensation Range: $249,600 - $312,000 Non-sales roles : In addition to base salary, this role is eligible to participate in DigitalOcean’s annual bonus plan and may be eligible for a new hire equity grant, subject to plan terms and local eligibility. Bonus amounts depend on company and individual performance. Sales roles (sales compensation eligible) : In addition to base salary, this role may be eligible to earn incentive compensation under the applicable sales compensation plan and may be eligible for a new hire equity grant, subject to plan terms and local eligibility. *This is a hybrid role JR: 2026-8161 #LI-Hybrid Why You’ll Like Working for DigitalOcean We innovate with purpose. You’ll be a part of a cutting-edge technology company with a high-growth trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As part of our team, you’ll embody our "Shark" mindset: bold, ambitious, and scrappy. You'll lead with a bias for action and take true ownership of our products, customer success, and team culture. We prioritize career development. At DigitalOcean, you’ll do the best work of your career. You will join a community of exceptional talent, collaborating closely with some of the brightest and most interesting people in the industry. We are a high-performance and fast-paced organization that will always challenge you to think big. Our talent development team prioritizes your professional development and provides resources to support your growth. We provide employees with reimbursement for relevant conferences, training, and education, subject to company policy. We care about your well-being. We take a comprehensive approach to our total rewards packages because we know different things drive different people. Our benefits are designed to support you and may include flexible time off, local team meetups, and mental health and well being support. Our benefits philosophy is consistent globally, but specific benefits and eligibility vary by location to reflect local needs and regulations. We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. Eligible employees may also receive equity compensation, which may include a new hire equity grant and, in participating countries, the opportunity to participate in our Employee Stock Purchase Program. Equity awards are subject to approval and the terms of the applicable plan. Non-sales roles : In addition to base salary, this role is eligible to participate in DigitalOcean’s annual bonus plan and may be eligible for a new hire equity grant, subject to plan terms and local eligibility. Bonus amounts depend on company and individual performance. Sales roles (sales compensation eligible) : In addition to base salary, this role may be eligible to earn incentive compensation under the applicable sales compensation plan and may be eligible for a new hire equity grant, subject to plan terms and local eligibility. DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, sex, disability, protected veteran status, or any other characteristic protected by applicable law. Application Limit: You may apply to a maximum of 3 positions within any 180-day period. This policy promotes better role-candidate matching and encourages thoughtful applications where your qualifications align most strongly.

Requirements

Must-haves

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
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
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
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
12+ years in large-scale compute, HPC, or GPU infrastructure engineering, including hands-on responsibility for systems in production
Deep understanding of GPU interconnect and system topology—NVLink and NVSwitch, NVLink domains, PCIe, NUMA, and how each shapes what a workload can be placed where
Strong RDMA networking background: RoCEv2 or InfiniBand at scale, GPUDirect RDMA, congestion control and lossless fabric tuning, and rail-optimized cluster topologies
Practical NCCL expertise—not just running it, but debugging it: algorithm and protocol selection, topology detection, and diagnosing collectives that are slow for non-obvious reasons
Substantial Kubernetes depth for accelerated workloads: device plugins and DRA, scheduling extensions, operators, and a clear-eyed view of where Kubernetes' model breaks down against rack-scale hardware
Strong systems programming and automation skills in Go and Python, and comfort at the driver, firmware, and BMC layer when the problem lives there
Experience operating fleets where hardware failure is routine, including the diagnostics, automation, and blast-radius thinking that makes that survivable
Excellent written and verbal communication, and a track record of leading architecture across organizational boundaries—hardware, networking, platform, and product
$249,600 - $312,000
This is a hybrid role
#LI-Hybrid Why You’ll Like Working for DigitalOcean
DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, sex, disability, protected veteran status, or any other characteristic protected by applicable law.

Nice-to-haves

Direct experience bringing up GB200 or GB300 NVL72 systems, or comparable rack-scale platforms, in a production environment
Experience with multi-node inference for very large models—expert or pipeline parallelism spanning NVLink domains, and the placement constraints that follow
Familiarity with liquid-cooled and high-density rack deployments, and the operational realities of servicing them
Contributions to relevant open source: Kubernetes SIG-Node or scheduling, NVIDIA GPU or Network Operator, DRA drivers, Kueue, LeaderWorkerSet, or NCCL
Experience running heterogeneous fleets spanning multiple accelerator vendors and generations under a single orchestration model

Application prep

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Keywords to mirror

kubernetesgopythonai

Platform: greenhouse
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Benefits

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Vision

Pto

Professional Development