CHI has built a full event operating system: public event discovery, ticket checkout, organizer dashboards, payment configuration, mobile tickets, QR scanning, crew operations, vendor sales, wallet flows, refunds, reporting, and settlement.
We need someone who prompts AI systems to create realistic event simulations, then maintains the code and test framework that makes those simulations executable.
This is not only writing tests. You will maintain the Playwright and Detox framework, improve fixtures and runners, inspect failures, and build self-healing workflows that suggest PRs after deployments or product code changes.
The goal is a test system that understands real event journeys, not just screens and buttons.
// The frontier
Turn prompts into production test coverage.
Self-healing event simulations
You will own the loop from prompt to generated scenario, executable test, failure analysis, and framework PR when the product changes intentionally.
// Ownership
What you will own
Prompt, run, and maintain AI-generated simulations across CHI Tickets, CHI PWA, CHI Backstage, and the native CHI app.
Maintain Playwright projects, Detox suites, page objects, fixtures, personas, setup flows, test data, traces, reporting, and CI integration.
Simulate event timelines from organizer setup through sales, payments, check-in, vendor sales, refunds, reporting, and payouts.
Cover countries, regions, time zones, currencies, languages, phone formats, addresses, dates, and translated journeys.
// Simulations
Example simulations
01
A 90-day international festival campaign with multilingual pages, ticket waves, VIP inventory, coupons, refunds, reminders, QR scanning, and reporting.
02
A high-volume ticket drop with payment failures, sold-out transitions, abandoned checkouts, duplicate attempts, limits, and reconciliation.
03
A multilingual buyer journey covering discovery, checkout, payment, confirmation, mobile tickets, transfers, refunds, and support states.
04
An event-day operations run with crew roles, vendor access, wrong-event tickets, cancelled tickets, offline conditions, wallet top-ups, and closeout.
// Self-healing
How the testing system should evolve
After a deployment, the AI agent should explain whether a failure is a product bug, stale selector, fixture drift, missing data, or missing coverage.
When the product change is intentional, the agent should suggest or open a clear PR for the required test or framework update.
Repeated failures should become better prompts, stronger fixtures, clearer traces, and more useful runbooks.
Coverage should expand from isolated flows into complete event simulations that run over long timelines.
// Bar
Required skills
Strong end-to-end testing experience with Playwright or a similar browser automation framework.
Strong TypeScript engineering skills and the ability to maintain a real test codebase over time.
Practical use of AI tools for test planning, generated data, debugging, coverage exploration, and scenario expansion.
Clear written communication for failure reports, reproduction steps, scenario definitions, and PR notes.
// Nice to have
Bonus signal
// Why CHI
Leverage that a normal team can't offer
Real stakes
Live events, live payments, live vendors. Your work runs in front of crowds, not in a sprint backlog.
Agents, not seats
Few humans, each given a workforce of agents. Your leverage is a multiple of a normal team's.
Full ownership
No ticket-shuffling, no three layers of approval. Product, backend, infra, and AI tooling in the same week.
The autonomous-company frontier
Most companies write think-pieces about AI-native teams. We are one — and you'll help define how it operates.
Skin in the game
Revenue share on top of base — when the platform earns, you earn. Each person's upside should look like it.
Bali base, remote functions
We keep an Indonesia/Bali office base for live collaboration, and support remote work where timezone overlap, ownership, and customer responsibility stay strong.
Event access
Our platform runs festivals. You'll see your work in the field.
Tooling budget
We pay for the AI tools and hardware that make you fast — including local-inference rigs alongside frontier APIs.
// Apply
Send your application to
[email protected]Your CV or LinkedIn profile.
A short note on how you use AI in testing or software delivery.
One complex end-to-end test scenario you designed, maintained, or would design for CHI.
Links to test code, tooling, or automation work if you can share them.
We care about ownership, precision, and the ability to turn messy real-world behavior into reliable executable simulations.