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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer, AI/ML - **Company:** DigitalOcean, LLC - **Location:** Bellevue, WA, United States - **Experience:** Expert - **Salary:** $216,800.0 - $271,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Python (Programming Language), Open Source Technology, Recommender Systems, Digitalocean, Software Engineering, Reinforcement Learning, Large Language Models, Multi-Agent Systems, Optimization Algorithms, Machine Learning Operations, Data Pipelines - **Published:** June 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=996dd81a01c5205f ## About the Role Do you have experience in Usability feedback collection?, Do you have a Master's degree?, 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. ## Description 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. ## Related Videos - [How AI Models Get Smarter](https://www.wearedevelopers.com/videos/1374-how-ai-models-get-smarter) - [100 million days in Vienna: A story of APIs & AI in tourism.](https://www.wearedevelopers.com/videos/93-100-million-days-in-vienna-a-story-of-apis-ai-in-tourism) - [Get security done: streamlining application security with Aikido](https://www.wearedevelopers.com/videos/1638-get-security-done-streamlining-application-security-with-aikido) - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Python-Based Data Streaming Pipelines Within Minutes](https://www.wearedevelopers.com/videos/1233-python-based-data-streaming-pipelines-within-minutes) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Got AI ideas but no money? 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