Director of Research, Agentic AI

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 building the Agentic Inference Cloud — anchored by Inference Engine, our model-serving platform — to help fast-growing AI-native companies start, scale, and optimize agentic workloads. Applied Research is the layer that generates the proprietary science that compounds across that platform: routing that learns instead of following static rules, memory that improves recall and personalization, observability that explains why agents succeed or get stuck, and reinforcement learning that closes the loop between production signals and model behavior.

• Define and own the applied research roadmap across adaptive routing (evolving model selection from static rules into a system that learns from real usage, cost, and latency), memory (retrieval quality and durable recall for long-running agents), agent observability (understanding when agents make progress, get stuck, or make mistakes), and reinforcement learning / closed-loop learning (turning production feedback into better models and policies).
• Track emerging model architectures and specialized, domain-tuned model approaches, and translate what's relevant into Digital Ocean's product roadmap.
• Keep the agenda tightly coupled to product outcomes — every research bet should map to a measurable improvement in model selection, agent reliability, cost/latency, or task-success rate, not research for its own sake.

• Grow the Applied Research team, hiring and mentoring research scientists and engineers.
• Establish the team's operating rhythm: how research questions get scoped, how experiments get run and evaluated, and how findings hand off to production teams.
• Represent Applied Research in cross-functional planning cycles alongside other engineering and product leaders, and make the case for headcount and investment on its own merits.

• Partner directly with Inference Engine and the other platform and product engineering teams that own agent runtime and evaluation infrastructure to turn research into shipped capability.
• Turn research prototypes into production-ready capabilities in partnership with engineering — shipping research, not just publishing it.
• Communicate research trade-offs clearly to non-research stakeholders, including when a promising direction isn't ready for product investment yet.

• Build Digital Ocean's credibility in the applied agentic-AI research community through publications, talks, open-source contributions, or collaborations — where they serve product and hiring goals.

• 10+ years in applied ML/AI research or research‑adjacent engineering, including experience leading a research team or function — formal people management or clear de facto technical leadership of a research group.
• Deep, hands‑on expertise in at least two of: LLM routing and model selection, retrieval and memory systems, agent observability and evaluation, or reinforcement learning (RLHF, RLAIF, DPO, PPO, GRPO, or related methods).
• A track record of shipping research into production systems — not just publishing or prototyping it.
• Fluency with the current agentic AI landscape: reasoning, planning, tool use, long‑horizon memory, and the practical…