Director of Research, Agentic AI

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 DigitalOcean'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 DigitalOcean'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 economics of large-scale model serving.

Strong technical communication — able to defend a research agenda to engineering leaders, product leaders, and executives, and to translate ambiguous research questions into a roadmap with concrete checkpoints.

Experience building or scaling an applied research team inside a product organization (not a pure research lab), with research investment justified in business terms.

Direct experience with inference infrastructure or large-scale model-serving optimization.

Familiarity with the broader open-weight and open-source model ecosystem.

PhD in CS, ML, or a related field — or equivalent depth demonstrated through industry impact.

Publications, patents, or open-source contributions in routing, memory, agent evaluation, or reinforcement learning.

Experience with small, specialized models or domain-tuned LLMs.

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.