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 the technical vision and roadmap for the memory and storage layer underneath DigitalOcean's inference platform. DigitalOcean is the Inference Cloud. The Inference Platform team builds the serving stack that runs frontier open models on our GPU fleet at production scale—model orchestration, disaggregated prefill and decode, request routing, and the engine integrations (vLLM, SGLang, TensorRT-LLM) that make it all go. As context lengths grow and agentic traffic patterns become the norm, the single biggest lever on cost, TTFT, and throughput is where the KV cache lives and how efficiently we can move it. That layer is currently a collection of good decisions made independently. This role exists to make it one system: a unified, multi-tier memory and storage substrate spanning GPU HBM, host DRAM, local NVMe, and remote object storage, exposed through a coherent interface to every serving engine and router we run. The selected candidate will set direction across teams, contribute meaningfully to the open source projects we depend on, and translate hard systems work into unit economics our customers feel. What You'll Be Doing: Defining and evolving a unified memory layer spanning GPU memory, pinned host memory, RDMA-accessible memory, local NVMe tiers, and remote object storage for large-scale LLM inference Architecting deep integrations with the serving engines we run in production (vLLM, SGLang, TensorRT-LLM), focused on KV cache offload, reuse, eviction policy, and cross-node sharing Designing the interfaces and protocols behind disaggregated prefill/decode, peer-to-peer KV cache transfer, and cache-aware routing—including how the router, the engine, and the cache tier agree on what is resident and where Owning the eviction and admission story across tiers (LRU and its successors, cost-aware policies, pluggable backends) and the metrics that prove those policies work under real traffic Partnering with our GPU infrastructure, networking, and platform teams to exploit GPUDirect, RDMA, NVMe-oF, and NVLink for low-latency cache access across heterogeneous accelerator pools Driving the unit economics: modeling and validating the effect of cache hit ratio, prefix caching, and tiering on $/token and $/GPU-hr, and making the tradeoffs legible to product and pricing partners Setting technical direction and raising the bar through design review, mentorship of senior and staff engineers, and sponsorship of the initiatives that follow from this roadmap Representing DigitalOcean externally—upstream contributions, conference talks, and customer-facing technical deep dives What You'll Add to DigitalOcean: 15+ years building large-scale distributed systems, high-performance storage, or ML systems infrastructure, with a track record of delivering and operating production services Deep fluency in memory hierarchies (GPU HBM, host DRAM, NVMe, remote/object storage) and experience designing systems that span tiers for both performance and cost Experience with distributed caching or key-value systems, particularly designs optimized for low latency under high concurrency Hands-on work with networked I/O and RDMA / NVMe-oF / NVLink-class technologies, and familiarity with aggregated and disaggregated deployment topologies for AI clusters Strong systems programming skills in C/C++, Go, Rust, or Python, and comfort reading and modifying serving-engine internals Rigor in profiling and optimization across CPU, GPU, memory, and network, using measurement to drive architectural decisions and to validate improvements in TTFT, ITL, and throughput Excellent written and verbal communication, and a history of leading cross-functional efforts with product, infrastructure, and customer-facing teams Bonus: Contributions to open source LLM serving or inference infrastructure projects—vLLM, SGLang, llm-d, NVIDIA Dynamo, LMCache, or similar—especially on KV cache offload, compression, or reuse Experience designing a unified memory or storage layer that presents a single logical KV or object model across GPU, host, SSD, and cloud tiers in a hyperscale or public cloud environment Familiarity with Kubernetes-based GPU orchestration, including DRA, MIG/MPS partitioning, and gateway/inference-extension routing patterns Publications or patents in LLM systems, memory-disaggregated architectures, RDMA-based data planes, or CDN-like caching systems for ML workloads 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: 2026-8160 #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

Defining and evolving a unified memory layer spanning GPU memory, pinned host memory, RDMA-accessible memory, local NVMe tiers, and remote object storage for large-scale LLM inference
Architecting deep integrations with the serving engines we run in production (vLLM, SGLang, TensorRT-LLM), focused on KV cache offload, reuse, eviction policy, and cross-node sharing
Owning the eviction and admission story across tiers (LRU and its successors, cost-aware policies, pluggable backends) and the metrics that prove those policies work under real traffic
Partnering with our GPU infrastructure, networking, and platform teams to exploit GPUDirect, RDMA, NVMe-oF, and NVLink for low-latency cache access across heterogeneous accelerator pools
Driving the unit economics: modeling and validating the effect of cache hit ratio, prefix caching, and tiering on $/token and $/GPU-hr, and making the tradeoffs legible to product and pricing partners
Setting technical direction and raising the bar through design review, mentorship of senior and staff engineers, and sponsorship of the initiatives that follow from this roadmap
Representing DigitalOcean externally—upstream contributions, conference talks, and customer-facing technical deep dives
15+ years building large-scale distributed systems, high-performance storage, or ML systems infrastructure, with a track record of delivering and operating production services
Deep fluency in memory hierarchies (GPU HBM, host DRAM, NVMe, remote/object storage) and experience designing systems that span tiers for both performance and cost
Experience with distributed caching or key-value systems, particularly designs optimized for low latency under high concurrency
Hands-on work with networked I/O and RDMA / NVMe-oF / NVLink-class technologies, and familiarity with aggregated and disaggregated deployment topologies for AI clusters
Strong systems programming skills in C/C++, Go, Rust, or Python, and comfort reading and modifying serving-engine internals
Rigor in profiling and optimization across CPU, GPU, memory, and network, using measurement to drive architectural decisions and to validate improvements in TTFT, ITL, and throughput
Excellent written and verbal communication, and a history of leading cross-functional efforts with product, infrastructure, and customer-facing teams
$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

Contributions to open source LLM serving or inference infrastructure projects—vLLM, SGLang, llm-d, NVIDIA Dynamo, LMCache, or similar—especially on KV cache offload, compression, or reuse
Experience designing a unified memory or storage layer that presents a single logical KV or object model across GPU, host, SSD, and cloud tiers in a hyperscale or public cloud environment
Familiarity with Kubernetes-based GPU orchestration, including DRA, MIG/MPS partitioning, and gateway/inference-extension routing patterns
Publications or patents in LLM systems, memory-disaggregated architectures, RDMA-based data planes, or CDN-like caching systems for ML workloads

Application prep

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Keywords to mirror

llmgomlairustpythonkubernetes

Platform: greenhouse
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Benefits

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Vision

Pto

Professional Development