Senior Engineer, Inference Data Plane

Senior Engineer, Inference Data Plane

Dive in and do the best work of your career rney 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.

Digital Ocean is expanding its AI Infrastructure layer to support the next generation of AI-driven applications. We are seeking a Senior Engineer 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.

• 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 Digital Ocean 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.

• 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 optimization techniques (e.g., continuous batching, quantization).
• Software Engineering:
  Expert-level proficiency in GoLang or Python and familiarity with gRPC.
• Cloud Operations:
  Pr…