Building AI agents that take real actions is the easy part. Building agents that get better over time — that learn from feedback, correct mistakes, and optimize toward outcomes users actually care about — is one of the hardest open problems in production AI today.

That's what this team works on. As a Staff AI/ML Engineer on our Applied Research team, you'll own the technical direction for feedback-driven learning in DigitalOcean's agentic systems: reward modeling, preference optimization, reinforcement learning, and the evaluation infrastructure needed to measure whether any of it is actually working.

This is a senior IC role with broad technical scope. You'll set direction, run experiments at scale, and close the loop between user signals and model behavior - shipping research into production, not just writing it up.

What You’ll Be Doing

Own the feedback learning roadmap

• Define and execute the applied research agenda for feedback-driven agentic AI — from reward modeling and preference optimization to online learning and human feedback loops.
• Translate user feedback, human evaluation data, and product signals into concrete training and optimization strategies.
• Stay close to the research frontier on RLHF, RLAIF, DPO, PPO, GRPO, and related methods and know when to apply them versus when simpler approaches win.

Build production learning systems

• Design and implement learning loops that improve agent reasoning, planning, tool use, and action execution over time.
• Build evaluation frameworks that measure what matters: reasoning quality, instruction following, task success, safety, and real user outcomes — at both offline and online scale.
• Run large-scale experiments that connect model changes to measurable improvements in user experience and business impact.

Provide technical leadership

• Set technical direction across modeling, experimentation strategy, evaluation design, and production readiness — without requiring direct management authority.
• Partner closely with product, engineering, design, and research teams to move work from prototype to shipped capability.
• Communicate complex AI systems clearly to both technical and non-technical stakeholders.

What You’ll Add to DigitalOcean

We're looking for engineers who have shipped real learning systems — not just prototyped them. You likely bring:

• 8+ years of experience building production AI/ML systems — LLMs, GenAI, agentic systems, recommendation, search, personalization, or applied research at scale.
• Hands-on experience improving AI systems through reinforcement learning, reward modeling, fine-tuning, human feedback, or preference optimization — with results you can point to.
• Strong understanding of agentic AI: reasoning, planning, tool use, action execution, instruction following, and self-correction.
• Strong software engineering in Python and at least one production systems language.
• The judgment to balance model quality, product impact, latency, reliability, cost, and maintainability — and communicate those tradeoffs clearly.

Preferred Qualifications

Strong signal

• Experience with agent evaluation, offline/online experiments, and human feedback loops in production.
• Direct experience with RLHF, RLAIF, DPO, PPO, GRPO, or related optimization techniques.
• Prior Staff, Senior Staff, Tech Lead, or equivalent senior IC experience.

Nice to have

• Master's or PhD in CS, ML, AI, or a related field — or equivalent depth demonstrated through industry work.
• Experience with production ML infrastructure: model serving, observability, data pipelines, feature stores, or experimentation platforms.
• Research contributions via publications, patents, open-source work, or demonstrated applied research impact in RL, reward modeling, evaluation, or recommendation systems.

Compensation Range: 

• $271,000 - $216,800

*This is a hybrid role

JR: 2026-7947

#LI-Hybrid