> Markdown version of [/jobs/ext/496884-learning-knowledge-systems-lead](https://www.wearedevelopers.com/jobs/ext/496884-learning-knowledge-systems-lead). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Learning & Knowledge Systems Lead - **Company:** Harpers & Company, Inc. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $110,000.0 - $170,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Cursor (Graphical User Interface Elements), Programming Tools, Knowledge Management, Knowledge-Based Systems, GPT - **Published:** June 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=48f32344ab9bb47c ## About the Role Do you have experience in Technical writing within technology?, * 3-8 years in a relevant field (see above). * Exceptional written communication and strong information-architecture instincts. * Real AI-tool fluency in practice: Cursor, Claude Code, MCP servers, agent memory files, Granola, structured prompting, AI-assisted synthesis. * Demonstrated ability to interview stakeholders, extract operational detail, and turn messy conversations into clear decisions and source-of-truth docs. * A track record of running an adoption rollout that actually changed how a team worked. * Comfort in a fast-moving, ambiguous startup. Based in San Francisco or willing to relocate. Nice to have: experience at an AI-native or dev-tools company; authoring Claude/agent skills or similar capability modules; RAG/search systems, data labeling, evals on knowledge systems, or human-in-the-loop QA; working with engineering on living-doc refresh automations; translating operator feedback into product requirements; taxonomy/metadata/content governance; insurance, fintech, B2B services, or another high-volume operational environment. The reality On-site in San Francisco, in the building with the operators whose knowledge you're extracting - this work doesn't happen remotely. The hours are long and the standards are high, because a rebuild this large doesn't happen part-time. The people who thrive here want it that way. If you want to walk into ambiguity, find the hidden logic, and turn it into systems the whole company runs against at AI speed, this is the seat. ## Description * Embed with sales, intake, service, placements, and renewals. Sit with operators, listen to calls, shadow workflows, and document what people "just know." * Turn transcripts, Slack threads, Looms, and one-off explanations into source-of-truth docs, decision logs, playbooks, process maps, glossaries, and system-boundary docs (what each internal system does, where one stops and the next begins, and what gets misread). * Graduate stabilized rules into skills the agents can call, and partner with engineering on refresh automations so docs stay alive instead of going stale. * Hunt the edge cases - reworks, escalations, stale quotes, market follow-ups, binder/payment gaps, customer confusion - and write them down before they bite again. Get the company to run against it. * When a rich AI-generated playbook lands, distill it into an executable plan: named owners, the first three moves, a rollout cadence and date. The plan doesn't run itself; you make it run. * Build the onboarding paths and setup scripts that get a new hire into Cursor, Claude Code, and the harness within a week. * Run cohort rollouts, drive adoption, and make activity visible - we should know who's actually in the harness. * Shape meetings in real time so they produce useful artifacts: decisions, owners, definitions, edge cases, open questions, next steps. * When the same problem shows up three times, turn it into a playbook, a QA check, a skill, or a product requirement. You'll work directly with the CEO when extraction calls for it. Who you are * An exceptional writer and synthesizer. You can take a messy transcript to a clear operating doc, and a clear doc to something a team actually executes against. * AI-native in practice - this is the bar. Not "I use ChatGPT." You have taste for when an output is structurally wrong, not just stylistically off. You prompt for extraction (decisions, contradictions, owners, edge cases), not just summarization. You know good output depends on good context, and you engineer that context upstream. Cursor, Claude Code, MCP servers, agent memory files, Granola, structured prompting are tools you reach for without thinking. * Genuinely curious about how organizations work, and happy to sit with operators to find out. * Structured but not bureaucratic. You care whether documentation changes behavior, not whether it looks polished. * Able to operate in chaos without becoming chaotic. Low-ego, persistent, and allergic to "someone should probably document that." Backgrounds that can work (the skill matters more than the title): modern enablement at an AI-native company, qualitative/academic research, ethnography, instructional or curriculum design, knowledge management, product ops, technical writing, research ops, implementation, chief of staff roles, library and information science, AI ops / human-in-the-loop work, or startup operations where the systems were messy and someone had to make them legible. ## Related Videos - [Same Tower, New Confusion: The Tower of Babel 2.0](https://www.wearedevelopers.com/videos/100101-same-tower-new-confusion-the-tower-of-babel-2-0) - [Evolving the developer experience in the age of AI](https://www.wearedevelopers.com/videos/100201-evolving-the-developer-experience-in-the-age-of-ai) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [AI Pair Programming with GitHub Copilot at SAP: Looking Back, Looking Forward!](https://www.wearedevelopers.com/videos/1546-ai-pair-programming-with-github-copilot-at-sap-looking-back-looking-forward) - [New AI-Centric SDLC: Rethinking Software Development with Knowledge Graphs](https://www.wearedevelopers.com/videos/1417-new-ai-centric-sdlc-rethinking-software-development-with-knowledge-graphs) - [Exploring AI: Opportunities and Risks in Development](https://www.wearedevelopers.com/videos/1267-exploring-ai-opportunities-and-risks-in-development) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Never delegate the understanding](https://www.wearedevelopers.com/magazine/749-never-delegate-the-understanding) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)