Staff Software Engineer, AI/ML

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DigitalOcean's Applied Research team is hiring a Staff AI/ML Engineer to own the technical direction for feedback-driven learning in its agentic systems. The role spans reward modeling, preference optimization, reinforcement learning, and evaluation infrastructure, shipping applied research into production. It is a senior individual contributor position with broad technical scope across modeling, experimentation, and production readiness.

• Define and execute the applied research agenda for feedback-driven agentic AI, including reward modeling, preference optimization, and online learning
• Translate user feedback, human evaluation data, and product signals into concrete training and optimization strategies
• Design and implement learning loops that improve agent reasoning, planning, tool use, and action execution over time
• Build evaluation frameworks measuring reasoning quality, instruction following, task success, safety, and user outcomes at offline and online scale
• Run large-scale experiments connecting model changes to measurable user and business impact
• Set technical direction across modeling, experimentation strategy, evaluation design, and production readiness
• Partner with product, engineering, design, and research teams to move work from prototype to shipped capability

• 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 demonstrable results
• 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
• Judgment to balance model quality, product impact, latency, reliability, cost, and maintainability

• 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
• Master's or PhD in CS, ML, AI, or a related field
• Experience with production ML infrastructure: model serving, observability, data pipelines, feature stores, or experimentation platforms
• Research contributions via publications, patents, open-source work, or applied research impact