HarperSan Francisco, CAThe Learning & Knowledge Systems Lead will transform tribal knowledge into structured, AI-legible information and ensure its operationalization. This role involves embedding with various teams to document workflows, authoring skills for AI agents, and driving the adoption of new playbooks and systems.
Two tracks, running in parallel. You'll sit at the intersection of Operations, Engineering, and RevOps.
Train the system. You embed with sales, intake, service, placements, and renewals. You sit with operators, listen to calls, study workflows, and turn what you find into source-of-truth docs, SOPs, playbooks, decision logs, system boundary docs, glossaries, and skills the agents can call. You partner with engineering on the automation layer so docs stay alive instead of going stale.
Train the people. When a playbook gets shipped, you're the one who turns it into an executable plan—named owners, first three moves, rollout cadence. You build the onboarding paths and setup scripts that get a new hire into Cursor, Claude Code, and the harness within a week. You run cohort rollouts, drive adoption, and make activity visible.
You don't need to be an engineer. You do need to be exceptional at using AI tools—Cursor, Claude Code, MCP servers, agent memory files, Granola, structured prompting—to turn raw context into reusable work product, and reusable work product into behavior change. You'll partner directly with the CEO when extraction calls for it.
This is not a note-taking role. This is not "make the Notion pretty." This is not corporate L&D. There is no LMS, no slide deck, no e-learning project. This is an operating role for someone who can walk into ambiguity, find the hidden logic, turn it into systems—and get the rest of the company to run against those systems at AI speed.
We're open-minded. Strong candidates might come from modern L&D or enablement at an AI-native company, qualitative or 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 roles where the systems were messy and someone had to make them legible.The exact background matters less than the ability to extract knowledge, impose useful structure, use AI tools well, and ship artifacts that change how people work.
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