Job description

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. 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. Preferred Qualifications 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. Compensation Range: $271,000 - $216,800 *This is a hybrid role JR: 2026-7947 #LI-Hybrid Why You’ll Like Working for DigitalOcean We innovate with purpose. You’ll be a part of a cutting-edge technology company with a high-growth trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As part of our team, you’ll embody our "Shark" mindset: bold, ambitious, and scrappy. You'll lead with a bias for action and take true ownership of our products, customer success, and team culture. We prioritize career development. At DigitalOcean, you’ll do the best work of your career. You will join a community of exceptional talent, collaborating closely with some of the brightest and most interesting people in the industry. We are a high-performance and fast-paced organization that will always challenge you to think big. Our talent development team prioritizes your professional development and provides resources to support your growth. We provide employees with reimbursement for relevant conferences, training, and education, subject to company policy. We care about your well-being. We take a comprehensive approach to our total rewards packages because we know different things drive different people. Our benefits are designed to support you and may include flexible time off, local team meetups, and mental health and well being support. Our benefits philosophy is consistent globally, but specific benefits and eligibility vary by location to reflect local needs and regulations. We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. Eligible employees may also receive equity compensation, which may include a new hire equity grant and, in participating countries, the opportunity to participate in our Employee Stock Purchase Program. Equity awards are subject to approval and the terms of the applicable plan. Non-sales roles : In addition to base salary, this role is eligible to participate in DigitalOcean’s annual bonus plan and may be eligible for a new hire equity grant, subject to plan terms and local eligibility. Bonus amounts depend on company and individual performance. Sales roles (sales compensation eligible) : In addition to base salary, this role may be eligible to earn incentive compensation under the applicable sales compensation plan and may be eligible for a new hire equity grant, subject to plan terms and local eligibility. DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, sex, disability, protected veteran status, or any other characteristic protected by applicable law. Application Limit: You may apply to a maximum of 3 positions within any 180-day period. This policy promotes better role-candidate matching and encourages thoughtful applications where your qualifications align most strongly.

Requirements

Must-haves

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.
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.

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpython

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Pto

Professional Development