Staff Software Engineer, AI/ML

Responsibilities

• 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.

Requirements

• 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.
• 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
• Why You'll Like Working for DigitalOcean
• We prioritize career development. At DO, you'll do the best work of your career. You will work with some of the smartest and most interesting people in the i

Benefits

Additional Information

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. 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.

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