AI Fullstack Engineer – Build Real-World Systems

Company: Revin

Location: New York, NY

Salary: $150,000 - $250,000 a year

Type: Full-time

Posted: 2026-04-23

About this role

## AI Fullstack Engineer – Build Real-World Systems

Location: New York, NY
Compensation: $150,000–$250,000
Employment: Full-time, On-site

Department: Engineering

### About Revin

At Revin, our mission is to bring super intelligence to the built world. Millions of operators across Home Services, Construction, and Remodeling have been overlooked by the last wave of technology. We believe AI is the most transformational platform change in history, but the hard work lies in thoughtful, context-aware applications of this technology to the real world.

Revin provides operators with an end-to-end system of intelligence to automate and scale revenue. Our modular suite of products includes Scheduling, Appointment Management, Membership Management, Inside Sales, Revenue Capture, Marketing, and Funnel Optimization.

Today, over $2.3B in revenue runs on Revin, making us the number one AI partner for enterprise operators. Our customers include Sears Home Services ($800M), Astara Capital ($700M), Del Air ($150M), Long Home Remodeling ($150M), ImproveIt Remodeling ($100M), Sage Home ($80M), and more. We have raised $10M from top investors including Stellation Capital, 1Sharpe, Gokul Rajaram, Russ Heddleston, Michael Werner, Tandem, Max Branzburg, and Marc Bhargava.

### About the Role

We’re hiring a Fullstack Engineer to build the systems that turn AI into reliable, real-world products used by operators running $100M+ businesses. This is a high-ownership, high-impact role for engineers who want to work on applied AI in the real world where reliability, performance, and user experience matter. You should be comfortable owning problems end-to-end — from backend systems to frontend UX — and shipping quickly in production.

You will work across the stack, from agent logic and data pipelines to customer-facing tooling and omni-channel delivery. You’ll work with messy, real-world data and systems — not clean datasets or controlled environments. You ...

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