Brands spend billions on Customer Data Platforms. We're rebuilding the category.

Lighter, faster, and live in weeks, not months. Built for the age of agents.

Paperbark maps the customer journey to stop churn for consumer brands. We use our own data models to connect tens of millions of customer events, and find the revenue that brands miss.

Responsibilities

- The quick path: from a visitor on a site to a decision in under half a second
- Agentic workflows that run in production for enterprise customers, with the checks that make them safe to trust
- Maintaining an enterprise-grade data warehouse and security controls in production
- Maintaining and improving our data pipeline infrastructure
- How we build: the stack, the standards, and the next hires

Qualifications

- You've managed big data systems in production for enterprise customers,



and you know what breaks at scale
- You've taken language-model or ML systems from prototype to production, and kept them running
- You care about latency, and can tell us where the milliseconds go
- You're eager to work with System 1 models: small, fast models that make a judgement in milliseconds instead of reasoning for minutes
- You already run parallel streams of agentic development, and you review what they ship before it goes out.

We expect it here

- You're happy to talk to a customer's engineering team directly
- You'd rather own a problem than a ticket
- Our stack is TypeScript and Python. Depth in one matters more than both

📌 Full Stack Engineer (Australia)
🏢 Paperbark
📍 Australia