Software Engineer

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.

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.