Founding Internal AI Systems Engineer

About Playabl

Playabl.ai (YC P26) is building the TikTok for user-generated games — a platform where anyone can create, publish, play, remix, and share AI-generated games.

About the Role

We are looking for a Founding Internal AI Systems Engineer to build the backbone of how Playabl operates internally. This person will own the systems that help the company understand users, detect product opportunities, debug issues faster, improve engineering workflows, and turn company knowledge into something queryable and actionable. This is not a traditional dashboard-building role. We are looking for someone who can build the internal AI infrastructure that makes the whole company faster. Playabl is a fast-moving AI-native company. As the product grows, the amount of internal context, product data, user behavior, bugs, feedback, and market movement will grow quickly. This role exists to make sure the company can see clearly, move quickly, and use AI as leverage across every part of the organization. You will be building the nervous system of the company.

What You Will Own

• A company brain that captures and organizes meetings, decisions, product discussions, user feedback, bugs, and investor/customer insights
• Internal AI agents that can answer questions about the business, product, users, games, prompts, engagement, and growth
• A data and observability layer connecting product data, logs, analytics, user behavior, comments, prompts, game performance, and internal context
• CLI-based tools that agents and engineers can use to safely query, analyze, and act on internal systems
• Internal workflows for bug triage, product feedback analysis, daily briefings, competitor monitoring, and engineering support
• Lightweight internal tools that help the team make decisions without overbuilding unnecessary dashboards
• Safe production workflows where agents can assist with debugging, data analysis, operational actions, and repetitive engineering tasks under human review

Responsibilities

• Design and build Playabl's internal company brain architecture
• Connect internal data sources into a reliable, queryable system
• Build tools for AI agents to interact with company data through safe, auditable interfaces
• Create daily and weekly internal briefings around product, users, bugs, competitors, and growth
• Build systems that summarize meetings, extract decisions, and turn discussions into useful context
• Help engineers debug issues faster by improving logs, observability, and internal tooling
• Build workflows that let team members ask questions in Slack or CLI and get useful answers
• Identify high-leverage internal processes that can be automated or agent-assisted
• Work closely with the founders and technical team to prioritize the highest-impact internal systems
• Maintain strong security boundaries around production data, user data, PII, and agent permissions

Qualifications

• Strong computer science fundamentals
• Experience building production systems before the current AI coding wave
• Strong backend engineering experience, ideally with TypeScript, Node.js, AWS, databases, queues, and APIs
• Experience working with AI agents, LLM workflows, internal tools, observability, or data infrastructure
• Good judgment around security, permissions, production access, and data privacy
• The ability to reason from first principles instead of blindly trusting AI-generated answers
• The ability to take vague company problems and turn them into simple, useful internal systems
• Strong product intuition and curiosity about user behavior
• A bias toward simple, high-leverage tools over overbuilt dashboards
• Comfort working directly with founders in a fast-moving startup environment

Nice to Have

• Instinct for spotting what's trapped in meetings, Slack, logs, and databases that should be made queryable
• A clear point of view on what should be automated versus kept human-reviewed
• Experience building things like credential/access managers, admin tools, moderation tools, or bug-triage systems that stay connected to shared company context instead of living as standalone panels
• Comfort defining an interview or take-home project from an ambiguous dataset and explaining how you would evaluate whether it is actually useful As part of the technical interview, we may share an anonymized sample database covering users, games, prompts, comments, engagement, and leaderboard activity, and ask you to build a small internal AI tool that helps answer questions like which prompts seem most unique, which prompts lead to the most engaging games, and what you would track daily if you were running the product. We care less about a polished UI and more about how you think, how you scope the problem, how you use AI, and how you evaluate whether the system is actually useful.