📍 Locations: San Francisco (Hybrid), New York (Hybrid), Columbus (Hybrid), or Remote (US) We’re hiring a Full Stack Engineer to shape how AI shows up across ReadMe’s product: from how developers explore and understand APIs to how companies author and maintain their documentation. We’re at the forefront of a major shift in developer experience: - AI agents need APIs to talk to each other, and those APIs need to be understandable, structured, and explorable - At the same time, writers and developers are looking for better ways to generate and maintain documentation with AI We're a small team of humans (and one owl http://owlbert.io/) working together to do big things — and that’s where you come in. In this role you’ll have a transformational impact on ReadMe across both the trajectory of the business and our thriving culture. 🦉 WHAT WE DO ReadMe helps more than 5,000 leading startups and tech companies build beautiful, personalized, and interactive developer hubs. If you’ve ever visited the developer docs for PagerDuty, Samsara, or Nvidia, you’ve used ReadMe! We love what we do because it’s so much more than just documentation. We’re providing tools for teams to build a better developer experience and make their products and APIs easier to use. We’ve got great support from our investors at Accel who led our Series A https://blog.readme.com/series-a/, and our interview process https://blog.readme.io/how-we-designed-our-interview-process/ reflects the open, caring, and whimsical culture we want to maintain as we scale. ✅ WHAT YOU’LL DO HERE This isn’t a feature team where a PM hands you tickets. You’ll help decide what gets built, why it matters, and how it works end-to-end. You’ll work on systems that are already live across thousands of developer hubs and define what they become next. ReadMe's AI surface spans three bets we're doubling down on: - Documentation that writes itself. AI that watches code change and keeps docs current — from PR-level suggestions to org-wide audits that surface and fix quality issues at scale. - Answers, not search results. A conversational layer embedded directly in developer hubs, grounded in real docs, that gives developers the right answer instead of a list of links to dig through. - APIs that are agent-ready. As AI agents increasingly talk to APIs, the structure and clarity of those specs matters more than ever. We're building the systems that make APIs understandable by both humans and machines. Day to day, you’ll: - Design end-to-end AI systems: combining LLMs, embeddings, retrieval pipelines, and structured outputs into reliable product features. - Build grounding and retrieval layers: indexing docs, OpenAPI specs, and customer data to support accurate, context-aware generation. - Define evaluation systems: measuring accuracy, latency, cost, and user impact; building feedback loops to continuously improve quality. - Handle real-world complexity: malformed specs, inconsistent docs, edge-case APIs, and ambiguous user queries. - Make architectural tradeoffs: latency vs. quality, cost vs. coverage, deterministic vs. generative approaches. - Own production reliability: observability, fallbacks, rate limiting, and safe degradation when systems fail. - Work across the stack: from model orchestration and backend systems to the UI surfaces that expose them. 💙 YOU'LL LOVE THIS JOB IF YOU... - Want to own meaningful systems end-to-end, not just features. - Care deeply about developer experience and documentation quality. - Enjoy working on problems where AI, APIs, and product design intersect. - Have strong instincts about what makes AI actually useful and where the industry is headed. ️ ⭐️ THIS ROLE IS A GREAT FIT IF YOU HAVE... - Experience building with LLMs in production, not just prototypes. - Strong JavaScript/TypeScript or Python experience. - Experience working with: - Embeddings and retrieval systems (RAG). - Prompting and structured outputs. - Evaluation and iteration of AI systems (quality, latency, cost). - Comfort working with APIs and structured data (JSON, OpenAPI, schemas). - Experience designing and shipping end-to-end systems, from backend pipelines to user-facing product surfaces. - Strong instincts around when to use AI vs. deterministic approaches. - Experience improving AI reliability, observability, and production readiness. - Familiarity with real-world edge cases (messy data, inconsistent inputs, ambiguous queries) and how to handle them. 🌱 HOW YOU'LL GROW WITHIN ONE MONTH... - Build a deep understanding of ReadMe’s AI systems and product surfaces. - Ship meaningful improvements to existing AI features. - Get close to real customer workflows and pain points. 🪴 WITHIN A FEW MONTHS, YOU'LL... - Own a core part of one of our AI systems. - Drive improvements in quality, reliability, and user experience. - Contribute to product and technical direction. 🌳 WITHIN YOUR FIRST YEAR, YOU'LL... - Be a key driver of AI across the product. - Ship systems used daily by thousands of developers and teams. - Help define how AI reshapes developer hubs and API workflows. 🛣️ WHAT'S THE HIRING PROCESS LIKE? 1. We can’t wait to read your resume and (hopefully personality-filled) cover letter! Let us know what excites you about full stack engineering, and help us get to know you better! 2. If we think we might be a good fit for you, we’ll set up a 30 minute Zoom call with our Davin, one of our Engineering Managers! She'll tell you more about the role, and get a chance to hear about your experiences. 3. Next will be a second 30 minute Zoom interview with our CEO and Founder, Greg, where we’ll dive a bit more into your background. 4. We’ll then do a technical assessment with a couple of ReadMe engineers. 5. Finally we’ll invite you to an "onsite" interview conducted over Zoom! These usually take 3.5 to 5