COMMAND DASHBOARD
Live signal — why now: Series C LLM-native customer-experience platform rebuilding its GTM engine; role explicitly emphasizes designing and shipping systems in Salesforce, Gong, and Claude.
Executive pain hypothesis: Most infrastructure does not yet exist; the company needs a hands-on architect who can build, test, and drive adoption.
Role mandate: Level AI is hiring for Revenue Operations Architect — GTM Systems Builder to own revenue systems, forecasting, and cross-functional operating cadence — the operational backbone connecting Sales, Marketing, and Customer Success.
Honest application risk: May require stronger direct Salesforce and data implementation evidence than the current resume shows.
Role details: Location: Remote, United States · Compensation: Not disclosed in reviewed posting

Near-direct match to Jay’s applied AI, conversational systems, sales operations, Claude workflows, and builder/operator identity. Use concrete demos of voice agents, talking websites, automated intake, and multi-agent operating infrastructure. The strategic gap is unambiguous: Level AI needs an operator who builds the revenue operating system from first principles — not one who merely maintains an inherited one.

Days 1–90Q1 — FOUNDATION
Days 91–180Q2 — BUILD
Days 181–270Q3 — SCALE
Days 271–365Q4 — OPTIMIZE
Conservative

Unified pipeline visibility and a reliable forecasting cadence established; manual reporting and triage materially reduced across Level AI's revenue team.

Target

Automated routing, qualification, and follow-up lift speed-to-lead and conversion; forecast accuracy and cross-functional operating cadence reach maturity.

Stretch

Repeatable revenue infrastructure scales Level AI's go-to-market output without proportional headcount, with real-time executive visibility across the funnel.

Strategic Summary

Core Opportunity

Level AI is hiring for Revenue Operations Architect — GTM Systems Builder at a decisive moment. Most infrastructure does not yet exist; the company needs a hands-on architect who can build, test, and drive adoption. Without dedicated ownership of the revenue operating system, that pressure compounds as the company grows.

Execution Thesis

Near-direct match to Jay’s applied AI, conversational systems, sales operations, Claude workflows, and builder/operator identity. Use concrete demos of voice agents, talking websites, automated intake, and multi-agent operating infrastructure. That is why this application is different — and why a conversation with Level AI is warranted now: Jay builds the operating system from first principles. Production systems, not theory.

Production systems, not theory. Revenue captured, not demos given.