Staff Software Engineer: AI Inference Data Plane

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. DigitalOcean is expanding its AI Infrastructure layer to support the next generation of AI-driven applications. We are seeking a Senior Engineer 2 to join our AI Inference Data Plane team. In this role, you will be a key technical leader responsible for designing, developing, and delivering high-scale, resilient data plane services that power our "Inference as a Service" offering. You will work at the intersection of distributed systems and specialized AI hardware to ensure our customers can deploy and scale their models with industry-leading performance and reliability. This is a hands-on role, requiring you to be able to develop high quality software while availing of all the productivity boosts granted by the latest AI coding agents. What You’ll Do: Technical Leadership: Act as a technical leader on the team, driving the end-to-end design, development, and delivery of critical data plane components hosting large generative AI models. System Design: Architect and refine system design proposals for our high-scale, multi-tenant AI inference cloud ecosystem, ensuring they meet rigorous availability and resiliency standards. Performance Optimization: Implement and optimize distributed inference hosting using techniques like tensor/data parallelism, KV cache optimizations, and smart routing. Collaboration: Work cross-functionally with Product Managers, customer-facing teams, and other engineering teams to align technical roadmaps with customer needs. Distributed Serving at Scale: Build on Kubernetes-native distributed inference frameworks like llm-d (or alternatives such as NVIDIA Dynamo, Ray Serve, KServe) to deliver prefill/decode disaggregation, KV-cache-aware routing, tiered prefix caching, and wide expert parallelism for MoE models. Flow Control & Load Balancing: Solve the distributed-systems problems unique to LLM serving — inference-aware load balancing on queue depth, cache locality, and predicted latency; flow control and fairness across tenants; autoscaling inference pools; and moving gigabytes of KV-cache between prefill and decode instances with negligible overhead. Open Source Contributions: Contribute upstream to llm-d, vLLM, and the inference gateway ecosystem, and represent DigitalOcean in these communities. Mentorship: Coach and mentor junior engineers, fostering a culture of technical excellence and continuous improvement. Operational Excellence: Maintain and operate critical, high-scale services, utilizing observability tools and defining SLOs to ensure superior platform health. What You’ll Bring to DigitalOcean: AI/ML Domain Knowledge: Hands-on experience hosting large language or multimodal models using inference engines like vLLM, SGLang, or TensorRT. Inference Frameworks: Familiarity with distributed inference serving frameworks such as llm-d, NVIDIA Dynamo, or Ray Serve. Inference Engine Depth: Hands-on experience with vLLM or alternatives (SGLang, TensorRT-LLM, TGI, Modular MAX), including internals like continuous batching, paged attention, and prefix caching. Distributed Inference Fluency: Understanding of why cluster-scale serving is hard: KV-cache locality is partitioned across workers, naive round-robin routing destroys cache hit rates and tail latency, and disaggregated prefill/decode requires fast cross-pod KV transfer (e.g., NIXL). Upstream Track Record: Merged contributions to vLLM, llm-d, SGLang, or similar projects strongly preferred. Architecture Proficiency: Knowledge of common LLM architectures and optimizatio…