Engineering Manager, Merchant

Description

Groupon connects 42 million customers with local experiences - restaurants, events, wellness and travel - and the million-plus merchants who deliver them. Our mission is to get people offline and into the real world at prices that make it possible. We're an AI-native company in the middle of a platform transformation, moving from a deals marketplace to an experience discovery platform that works for customers and merchants at the same time.

Every deal Groupon sells passes through one internal platform — Groupon IQ — where deals are created, checked and published. Seven engineers run it, and AI agents write much of the code. It's absorbing the company's other admin tools one by one; a deal lookup that used to take two minutes now takes thirty seconds. Nobody yet owns its reliability, or decides when agent-written code is safe to ship. You'd own both — and still be in the code.

What you'd actually get to build

Groupon IQ already runs on the company's modern engineering foundation — the platform the rest of the org is still migrating onto. That's your advantage: you build on the finished thing while making it load-bearing for everyone.

One TypeScript codebase — ~95% of it, in a single repository, on the framework built by ex-Netflix engineers (Encore).

Infra without tickets — declare a database, cache, or queue in a line of code and it's provisioned, wired, and observed for you: 60+ services stood up without a single infra request.

Ship all day — every branch is a full staging environment; one pull request can change 23 services and two frontends and go out as a single deploy. Merges reach production the same day — minutes when it matters.

A workflow engine (Temporal) runs the long processes — purchase flow, deal creation, outreach — reliably and auditably.

AI is native — a shared model gateway and coding-assistant rules wired into every service. Engineers here average ~7 hours of real coding a day (versus ~3 elsewhere at Groupon); agents write real code, and review keeps it honest.

Who you'll work with

You'll report to Pavel Jakl (B2B Tribe Director), who writes code himself — a player-coach seat, not a calendar-and-1:1s one. You'll lead ~7 engineers across Prague and Bangalore in one codebase. Here, reliability is the product : as IQ absorbs more of the company's tools, more teams depend on it not breaking. Not for you if you want to stop coding, or want a pre-scoped backlog with someone else on the pager. Right for you if you like making a load-bearing platform faster and more reliable as it scales — and deciding how humans and agents build on it.

A week in the life

Backend reliability, end to end — a long-running workflow stalls silently; you trace it through the platform's observability (no ticket, no waiting), fix it in one pull request, and write the post-mortem that stops it recurring.

Production code, daily — real output, not a title.

The AI code-review bar — recently an agent traced intermittent database timeouts to a call that never waited for its job to finish, moved every path onto the shared polling client, and added the tests that were missing: agent-written, human-reviewed, shipped. You own the standard that keeps work like that safe to merge.

Absorbing the next tool — IQ is consolidating the company's admin tools; you make each one land without dragging the whole platform down.

Cross-team architecture calls — you make them and defend them.

Mentoring — seven engineers toward end-to-end ownership; feedback within a day, standards written down.

Signals you're a fit

There's been a week where your commits and your team's output were both on the line — and both shipped.

You've owned a platform where reliability was the product, and you moved the incident number.

You've reviewed agent-written code and decided where the merge bar sits.

You've traced a silent workflow failure end to end and written the post-mortem that stopped it recurring.

You've automated away a manual task and spent the freed time building the next thing.

We'll dig into these live — bring the specifics. (You're still shipping, so show us something you built recently where agents wrote much of the code, and how you kept it correct.)

What success looks like

Day 0–30: met everyone, mapped the incident history, shipped a real backend fix, started instrumenting a reliability baseline (there isn't a trustworthy one today).

Day 31–60: the baseline is trustworthy; the AI code-review standard is live; one named slice of reliability debt closed.

Day 61–90: incident rate measurably down against your baseline; an agent runs continuously, taking a manual task off the team; the next admin-tool migration lands without a reliability hit.

How we operate

Extreme ownership — you own the fix and the post-mortem, not the ticket that reassigns it.

Impact-obsessed — incident rate and coding hours are numbers you move, with a before and after.

AI-first and disciplined — push agents to do more each month; hold the review bar that keeps them safe. Agency, not brilliant-jerk.