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 DoingOwn 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.
- $271,000 - $216,800
*This is a hybrid role
JR: 2026-7947
#LI-Hybrid
- We innovate with purpose. You’ll be a part of a cutting-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.
- 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 industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.
- We care about your well-being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.
- We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.
- DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
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.
Skills Required
- 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.
- Strong understanding of agentic AI: reasoning, planning, tool use, action execution, instruction following, and self-correction.
- Strong software engineering in Python.
- Experience with at least one production systems language.
- Ability to translate user feedback and product signals into training and optimization strategies and ship research into production.
- 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 related field (or equivalent industry depth).
- Experience with production ML infrastructure: model serving, observability, data pipelines, feature stores, or experimentation platforms.
- Research contributions (publications, patents, open-source) in RL, reward modeling, evaluation, or recommendation systems.
DigitalOcean Compensation & Benefits Highlights
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Healthcare Strength — Health coverage includes medical, dental, vision, and mental-health support plus employer-paid life/AD&D/disability, with multiple plan options and above-average employer contributions. Offerings are described as market-leading and in some cases fully paid to keep premiums low.
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Equity Value & Accessibility — Equity awards (new-hire and performance RSUs) are paired with an Employee Stock Purchase Plan offered at a discount. This ownership component augments cash compensation and broadens participation in company growth.
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Parental & Family Support — Paid parental leave is provided for a defined period and includes a structured, part-time transition-back program. The approach emphasizes a smoother return-to-work experience for new parents.
DigitalOcean Insights
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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DigitalOcean Offices
Remote Workspace
Employees work remotely.
We commit to both remote work and in-person collaboration. These ways of working are dependent on specific roles and are mutually agreed upon by employees. In the US, we are mainly remote. In our APAC locations, we have a hybrid in-office approach.
