<div class="content-intro"><p>Dive in and do the best work of your career at DigitalOcean. Journey 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. </p></div><p>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.</p> <p>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.</p> <p>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.</p> <h2><strong>What You’ll Be Doing</strong></h2> <p><strong>Own the feedback learning roadmap</strong></p> <ul> <li>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.</li> <li>Translate user feedback, human evaluation data, and product signals into concrete training and optimization strategies.</li> <li>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.</li> </ul> <p><strong>Build production learning systems</strong></p> <ul> <li>Design and implement learning loops that improve agent reasoning, planning, tool use, and action execution over time.</li> <li>Build evaluation frameworks that measure what matters: reasoning quality, instruction following, task success, safety, and real user outcomes — at both offline and online scale.</li> <li>Run large-scale experiments that connect model changes to measurable improvements in user experience and business impact.</li> </ul> <p><strong>Provide technical leadership</strong></p> <ul> <li>Set technical direction across modeling, experimentation strategy, evaluation design, and production readiness — without requiring direct management authority.</li> <li>Partner closely with product, engineering, design, and research teams to move work from prototype to shipped capability.</li> <li>Communicate complex AI systems clearly to both technical and non-technical stakeholders.</li> </ul> <h2><strong>What You’ll Add to DigitalOcean</strong></h2> <p><strong>We're looking for engineers who have shipped real learning systems — not just prototyped them. You likely bring:</strong></p> <ul> <li>8+ years of experience building production AI/ML systems — LLMs, GenAI, agentic systems, recommendation, search, personalization, or applied research at scale.</li> <li>Hands-on experience improving AI systems through reinforcement learning, reward modeling, fine-tuning, human feedback, or preference optimization — with results you can point to.</li> <li>Strong understanding of agentic AI: reasoning, planning, tool use, action execution, instruction following, and self-correction.</li> <li>Strong software engineering in Python and at least one production systems language.</li> <li>The judgment to balance model quality, product impact, latency, reliability, cost, and maintainability — and communicate those tradeoffs clearly.</li> </ul> <h2><strong>Preferred Qualifications</strong></h2> <p><strong>Strong signal</strong></p> <ul> <li>Experience with agent evaluation, offline/online experiments, and human feedback loops in production.</li> <li>Direct experience with RLHF, RLAIF, DPO, PPO, GRPO, or related optimization techniques.</li> <li>Prior Staff, Senior Staff, Tech Lead, or equivalent senior IC experience.</li> </ul> <p><strong>Nice to have</strong></p> <ul> <li>Master's or PhD in CS, ML, AI, or a related field — or equivalent depth demonstrated through industry work.</li> <li>Experience with production ML infrastructure: model serving, observability, data pipelines, feature stores, or experimentation platforms.</li> <li>Research contributions via publications, patents, open-source work, or demonstrated applied research impact in RL, reward modeling, evaluation, or recommendation systems.</li> </ul> <h2><strong>Compensation Range: </strong></h2> <ul> <li>$271,000 - $216,800</li> </ul> <p><span style="font-weight: 400;">*This is a hybrid role</span></p> <p><span style="font-weight: 400; color: rgb(0, 0, 0);">JR: 2026-7947</span></p> <p><span style="color: rgb(0, 0, 0);"><em><span style="font-weight: 400;">#LI-Hybrid</span></em></span></p><div class="content-conclusion"><h2><strong>Why You’ll Like Working for DigitalOcean</strong></h2> &l