Lead Software Engineer (AI-first, Encore)

Confirmed live in the last 24 hours

Groupon

Madrid; Prague

Hybrid

Posted April 14, 2026

Job Description

Groupon is a marketplace where customers discover new experiences and services everyday and local businesses thrive. To date we have worked with over a million merchant partners worldwide, connecting over 16 million customers with deals across various categories. In a world often dominated by e-commerce giants, we stand out as one of the few platforms uniquely committed to helping local businesses succeed on a performance basis.

Groupon is on a radical journey to transform our business with relentless pursuit of results. Even with thousands of employees spread across multiple continents, we still maintain a culture that inspires innovation, rewards risk-taking and celebrates success. The impact here can be immediate due to our scale and the speed of our transformation. We're a "best of both worlds" kind of company. We're big enough to have the resources and scale, but small enough that a single person has a surprising amount of autonomy and can make a meaningful impact.

About the Role

Lead Software Engineer - Core Operations Services (AI-first, Encore): This is a technical leadership role in Groupon's core commerce systems handling 200K+ requests per minute, with significant contribution to our AI transformation.

Core Operations Services powers Groupon's global marketplace — API gateways, deal catalogs, user identity, and image services — running across Java, Ruby, and TypeScript stacks. You'll contribute hands-on to the migration to TypeScript on Encore, collaborate across teams, and operate natively in an AI-driven development environment.

Key Responsibilities

Shape technical strategy with leadership for high-availability systems.
Lead cross-team design reviews and migrations to TypeScript / Node.js.
Drive the team's AI-native development practice — author the setups, patterns, and guardrails the rest of the team adopts. Your role is to raise the AI maturity of the team, not ramp up on it.
Articulate the importance of code quality in business language.
Optimize strategies to maintain SLOs, observability, and documentation.

Must Have

5+ years engineering at production scale, serving 200K+ RPM on distributed systems.
Production depth in at least one of: Java, Ruby, or TypeScript / Node.js. Adjacent stacks (C#/.NET, Go, Python-only) do not qualify — you have shipped and operated services in one of the three.
Hands-on migration of large production systems using strangler fig, facade, or dual-running patterns. You can describe a migration you owned end-to-end, including rollback strategy, traffic cutover, and data-consistency approach.
Drives AI-native development at team scale. You use Claude Code (or a directly comparable agentic tool) daily, and you've authored setups other engineers have adopted — MCPs, skills / agents, hooks, evaluation patterns, prompt libraries. You can point to guardrails you've defined and enforced — review protocols, cost controls, data-handling policies, quality gates for AI-generated code. The bar here is practice-setter, not competent user.
API design and observability — SLOs, traces, structured logs, on-call ownership.
Strong written and verbal communication across technical and non-technical audiences.

Nice to Have

E-commerce or marketplace platforms.
Open source contributions or AI / ML production experience.
Prior experience with Encore, Temporal, or similar workflow frameworks.

What Success Looks Like

Architectural decisions that accelerate team velocity.
Gradual modular migration with planned, minimal-downtime cutovers.
First 30 days: operating in the team's AI workflow on day one — not ramping up on it.
First 90 days: team AI maturity has measurably moved up — setups you've authored are in use by other engineers, guardrails you've defined are enforced in review, and the team ships AI-assisted changes with more confidence than before you joined.
Clear documentation and alignment with business outcomes.

We Are Interested In

Experienced engineers with deep roots in Java, Ruby, or TypeScript who have personally led migrations of large production systems, drive AI-native development as a practice leader (authoring setups, defining guardrails, rais

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