AI Operations & Innovation Lead

We're hiring an AI Operations & Innovation Lead!

Summary

• Flexible Title: Can tailor to reflect your skills & experience — from AI Integration Specialist to Head of AI Operations.
• Flexible Time: Can do full-time, part-time, side-gig (off-hours), or fractional (contract).
• Flexible Commitment: Can do short-term, long-term, or intermittent.

Why so flexible? We're a FUNDED startup racing to launch end of Q1 2026. That gives us just 3 months to stack features while raising additional working capital. Feel free to jump in, help us ship, then bounce >> or stick around. A successful launch translates into lots of permanent jobs for those that want them. We're also interested in long term "side gig" relationships, if that's what you're into - in our experience, a few expert hours often beat full-time learning-curve hours.

About Us

We're a credible, funded , remote-first startup led by a serial [technical] founder, and backed by a 20-person team. The product is live in private alpha.

Learn more about our founder, team, and comp structures at list-lab.org .

About The Role

This isn't an automation role — automation is table stakes. This is about pushing the envelope on AI integration and building systems that make an AI-native team even smarter.

We're looking for someone who sees this as an opportunity to experiment, innovate, and write about it . You'll have the freedom to try new approaches, fail fast, and publish your learnings. No red tape, no bureaucratic approval chains — just a fast-moving team that wants to see what's possible.

Here's where we are today:

Our software devs have already experimented with using AI bots (Runbear) to digest status updates and meeting transcripts, generating daily, weekly, and monthly rollups. It works. But it's just the beginning.

Here's where we want to go:

We want to build a system that extracts and structures all this information — meeting transcripts, status updates, project management data from ClickUp, and more — into a persistent, organized knowledge base (think git repo or shared drive). The goal: make this structured data available for intelligent analysis and reporting via Cursor/Claude , and accessible to staff as shared files — not just ephemeral bot responses.

Imagine: any team member can query the full context of a project, get AI-generated insights on velocity trends, surface blockers before they're escalated, or generate investor-ready updates on demand. That's the vision.

What Success Looks Like in 30 Days

• You've mapped our AI tooling and data flows, designed the knowledge base architecture, and shipped a working system that ingests data from multiple sources
• Staff can access structured project context as shared files
• At least one AI-powered analysis workflow is live (e.g., weekly rollups, blocker detection)
• You've identified and queued the next wave of high-leverage integrations

What Success Looks Like in 60 Days

• The system is a core part of how the team operates
• AI-generated insights are surfacing actionable information proactively
• You've shipped several experiments and itterated based on team feedback
• You've documented the architecture and published at least one external post on your learnings

What Success Looks Like in 90 Days

• The knowledge system is mature, scalable, and self-maintaining
• You're advising on the next frontier of AI integration — agents, proactive assistants, or novel applications
• Your work has become a competitive advantage and recruiting asset for the company
• You're recognized internally (and ideally externally) as a thought leader in AI operations

What You'll Do

• Design and build systems that extract, structure, and persist operational data for AI consumption
• Integrate data sources: Slack, ClickUp, meeting transcripts, status updates, and more
• Create AI-powered analysis and reporting workflows using tools like Cursor, Claude, and custom integrations
• Make structured knowledge accessible to the team as files, not just bot responses
• Experiment with new AI tools, techniques, and architectures — and share your learnings
• Collaborate with engineering and leadership to identify high-impact AI integration opportunities
• Document your work and (optionally) publish blog posts or case studies on your innovations
• Stay on the bleeding edge of AI tooling, agents, and knowledge management

What We're Looking For

• Hands-on experience with AI/LLM integrations — you've built systems that leverage GPT, Claude, or similar models in production
• Systems thinker — you see data flows, not just individual tools
• Technical enough to build — Python, JavaScript, or similar; comfortable with APIs, webhooks, and data pipelines
• Experience with knowledge management, data extraction, or information architecture
• Familiarity with tools like Slack, ClickUp, Notion, Obsidian, git-based knowledge systems, or similar
• Curious and experimental — you're excited by ambiguity and the chance to try new things
• Strong communicator — you can explain complex systems simply and document your work clearly
• Self-directed and proactive — you don't wait for permission to innovate
• Bonus: You've written publicly about AI, automation, or knowledge systems

Why This Role is Different

Most companies talk about AI but move at a glacial pace. We're an AI-native team that's already experimenting — we just need someone to take it to the next level. You'll have:

• Freedom to experiment without layers of approval
• A team that gets it — engineers and leadership who understand AI and want to push boundaries
• The opportunity to publish your work and build your personal brand
• Real problems to solve — not theoretical exercises, but systems that make a growing team more effective

If you've been waiting for the right environment to truly innovate with AI, this is it.