Director of Research, Agentic AI
$250k - $312k • Seattle • Full-time
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Agent Engineer
About the job
DigitalOcean is building the Agentic Inference Cloud, a platform designed to help AI-native companies scale and optimize agentic workloads. The Applied Research team is central to this effort, generating proprietary science that enhances the platform's capabilities in areas like adaptive routing, memory, observability, and reinforcement learning. We are seeking a Director of Research to lead this function, setting the research agenda, growing the team, and ensuring that scientific advancements translate into tangible product improvements, such as better model selection, more reliable agents, and reduced costs and latency for users.
Responsibilities
- Define and own the applied research roadmap across adaptive routing, memory systems, agent observability, and reinforcement learning.
- Track emerging model architectures and specialized model approaches, translating relevant findings into the product roadmap.
- Ensure research efforts are tightly coupled to product outcomes and measurable improvements.
- Grow and lead the Applied Research team, including hiring and mentoring research scientists and engineers.
- Establish the team's operating rhythm for scoping research questions, running experiments, and evaluating findings.
- Represent Applied Research in cross-functional planning and advocate for team investment.
- Partner with engineering teams to translate research prototypes into production-ready capabilities.
- Communicate research trade-offs clearly to non-research stakeholders.
- Build DigitalOcean's credibility in the applied agentic-AI research community through publications, talks, or collaborations.
Requirements
- 10+ years in applied ML/AI research or research-adjacent engineering, with experience leading a research team.
- 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.
- Proven track record of shipping research into production systems.
- Fluency with the current agentic AI landscape, including reasoning, planning, tool use, and long-horizon memory.
- Strong technical communication skills to defend research agendas and translate ambiguous questions into roadmaps.
- Experience building or scaling an applied research team within a product organization.
- Direct experience with inference infrastructure or large-scale model-serving optimization.
- Familiarity with the broader open-weight and open-source model ecosystem.
Benefits
- Annual bonus plan
- New hire equity grant
- Hybrid work arrangement