Staff Software Engineer AI/ML

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.

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.

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.

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.

• $271000 - $216800

*This is a hybrid role

JR: 2026-7947

#LI-Hybrid

What We Do

DigitalOcean is the Inference Cloud — a full-stack production-ready cloud platform built to run AI applications with predictable performance sustainable economics and radically simpler operations at scale. We are built for teams turning AI into real products — not just training models. Our advantage is not fewer features but fewer failure modes when operating AI at scale — combining minimal operational overhead predictable cost efficiency and a full-stack cloud that works as a system. Hyperscalers are broad by design. Neoclouds are infrastructure-first. DigitalOcean is inference-first — with a real cloud underneath. It combines inference-optimized compute managed inference software and integrated cloud capabilities that reduce operational burden for teams running real workloads. Inference is the foundation—not the boundary. Everything else builds on top of it.

Why Work With Us

At DO we do career-defining work. We innovate with AI and build cutting-edge tech. Our rewards to match that intensity - to motivate you recognize your impact and give you what you need to thrive. If you have a growth mindset like to think big and bold and are energized by the fast-paced environment you'll find your place here.

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