Founding Internal AI Systems Engineer

Role overview A founding-level engineering role focused on building the internal AI infrastructure that powers how the company operates day to day. This is not a dashboard-building position; the work centers on creating the central nervous system of a fast-moving AI-native organization, turning scattered context such as meetings, product data, user behavior, bugs, and feedback into queryable, actionable intelligence. The engineer will own the systems that accelerate decision-making, debugging, and product discovery across the entire company.

Responsibilities - Design and own the architecture for an internal knowledge system that captures meetings, decisions, product discussions, user feedback, bugs, and external insights - Connect disparate internal data sources into a reliable, queryable system spanning product data, logs, analytics, user behavior, prompts, and game performance - Build safe, auditable interfaces, including CLI tools, that let AI agents and engineers query, analyze, and act on internal systems - Develop internal AI agents that can answer questions about the business, product, users, engagement, and growth - Create daily and weekly automated briefings covering product, user, and competitive signals, along with internal workflows for bug triage and feedback analysis - Implement safe production workflows where agents assist with debugging, data analysis, and operational tasks under human review

Requirements - Strong background in building internal AI tooling, data systems, or agent infrastructure - Experience connecting heterogeneous data sources into unified, queryable systems - Comfort designing safe, auditable interfaces for AI-driven automation - Curiosity about user behavior and 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 valuable context trapped in meetings, chat logs, and databases that should be made queryable - A clear point of view on what should be automated versus kept under human review - Experience with credential or access managers, admin tools, moderation tools, or bug-triage systems that stay connected to shared company context - Comfort defining interview or take-home projects from ambiguous datasets and explaining how you would evaluate their actual utility

Benefits and work setup - Fully remote, full-time position within the engineering organization