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Principal Engineer, Inference Memory and Storage Systems at DigitalOcean, Seattle, WA

Principal Engineer, Inference Memory and Storage Systems at DigitalOcean in Seattle, WA. $249,600 to $312,000 a year. Apply with swipeandhired.

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

*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 an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.

We prioritize career development. At DO, you’ll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.

We care about your well-being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.

We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.

DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

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.