About this role
Leaders who enjoy bridging the gap between theoretical science and production systems will thrive in this role at DigitalOcean. The team is building the Agentic Inference Cloud, which relies on the Inference Engine model-serving platform to help AI-native companies manage agentic workloads. As the Director of Research, this person will establish the applied research agenda, grow the team, and ensure scientific breakthroughs translate directly into reliable model routing, enhanced memory, and improved task success rates.
Operating in a hybrid structure based in Seattle, the incoming director needs sufficient technical depth to evaluate research across routing, memory, observability, and reinforcement learning alongside the leadership capability to guide cross-functional execution. Success in this position requires a balance of rigorous scientific inquiry, strategic roadmap prioritization, and the communication skills needed to defend research investments to executive stakeholders.
What you will do
- Define and own the applied research roadmap across adaptive routing, durable memory systems, agent observability, and reinforcement learning.
- Grow and mentor the Applied Research team by hiring research scientists and engineers while establishing clear operational rhythms for experiment evaluation.
- Partner directly with Inference Engine and platform engineering teams to transition research prototypes into production-ready capabilities.
- Represent DigitalOcean externally within the applied agentic-AI research community through publications, talks, or open-source contributions.
- Communicate research trade-offs clearly to non-research stakeholders and make the case for headcount and investment during cross-functional planning cycles.
What you need
- Bring 10 or more years of experience in applied machine learning research or research-adjacent engineering, including formal or de facto leadership of a research team.
- Possess deep, hands-on expertise in at least two areas covering LLM routing, retrieval and memory systems, agent observability, or reinforcement learning methods like RLHF, DPO, or PPO.
- Demonstrate a proven track record of shipping research outcomes directly into production systems rather than solely prototyping or publishing.
- Maintain fluency with the current agentic AI landscape, including reasoning, planning, tool use, long-horizon memory, and model-serving economics.
- Exhibit strong technical communication skills to defend research agendas and translate ambiguous questions into concrete engineering checkpoints.
Nice to have
- PhD in Computer Science, Machine Learning, 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
- Experience building or scaling an applied research team inside a product organization
- Direct experience with inference infrastructure or large-scale model-serving optimization
Benefits
- Flexible time off policy
- Employee Assistance Program
- Reimbursement for relevant conferences, training, and education
- Access to LinkedIn Learning courses
- Equity compensation and Employee Stock Purchase Program
- Bonus eligibility based on company and individual performance
Skills and tech stack
Questions candidates ask
Is this a remote position?
This is a hybrid role by design, based in Seattle. It requires enough technical depth to personally evaluate and shape research direction alongside leadership range to build a team.
What is the compensation range for this role?
The salary range for this position is $249,600 to $312,000, determined by market data, relevant years of experience, and skills. Employees may also qualify for performance-based bonuses and equity compensation.
What will I actually be working on?
You will set the applied research roadmap across adaptive routing, memory systems, agent observability, and reinforcement learning. Your team will turn research prototypes into production-ready capabilities in partnership with engineering.
What qualifications are required for the role?
Candidates need 10+ years in applied ML/AI research or research-adjacent engineering, including experience leading a research team. You must also have hands-on expertise in at least two areas like LLM routing, memory systems, agent observability, or reinforcement learning.