Engineer

PLP Group; Warszawa; Hybrid; PLN 140,000 - 240,000; Full time

Job summary

Klarna is seeking an engineer to own real production code from day one, tackling vaguely defined problems, and learning a large codebase quickly. You will document the parts that aren’t written down and argue for clear decisions rather than plans. You’ll use AI as a working method and collaborate through code reviews.

The role emphasizes hands-on coding, fast learning, and willingness to be the least experienced person in the room, with co-located teams and flexible office presence.

Qualifications

• You've built things beyond what a course, a job description or an assignment required.
• You can talk about why you chose to build something and explain a system you built.

Responsibilities

• Own real production code from day one and ship it.
• Ask for review, act on it, and give it back.
• Learn a large unfamiliar codebase faster than comfortable.
• Document the part nobody had written down.

Skills

Production code

Code review

Fast learner

Documentation

Job description

Klarna, briefly At Klarna, we're building an everyday finance network, helping over 120 million consumers across 26 countries save time and money, and worry less about their finances. Working here means taking on problems most companies never get to solve, and being hands-on enough that the interesting part of the work lands with you, not someone else — you'll build with AI, not watch it happen. This is the stretch zone. Come find out what you're capable of.

About the role

Being an engineer at Klarna means building and owning the systems that move money for millions of people — and that responsibility doesn't end when those systems ship. Your team and your focus can also change as the business changes, and the domain you join may look different in a year. We are searching for someone whose work is already ahead of where their experience says it should be - that means we care more about what you've built than how long you've been building. We want to hear about a problem you spotted yourself and then solved.

What you'll do

Own real production code from day one. Take a problem that's vaguely defined, ask the questions that sharpen it, and come back with something working rather than something planned. Learn a large unfamiliar codebase faster than feels comfortable, and be the person who then documents the part nobody had written down. Make small decisions well and say what you decided against - the habit matters more at this stage than the size of the decision. Use AI as a working method rather than a demo, and know why the output is right before you ship it. Ask for review, act on it, and give it back. The engineers here will spend time on you; the trade is that you use it.

Who you are

You've built things beyond what a course, a job description or an assignment required, and you can talk about why you chose to. You learn fast and you can show it - something you knew nothing about six months ago that you're now the go-to person for. You can explain a system you built to someone outside your field. You're specific about what you personally did versus what your team or your tooling did. You're comfortable being the least experienced person in the room and asking the question anyway. You've had something go wrong that was yours, and you can say what you changed afterwards. You want to be pushed. This is not a role for someone optimising for comfort.

Things you should know before applying

Working together: We value co-located teams; most teams currently meet in the office 2–3 days per week, and this varies by team and can change over time. Non-obvious backgrounds are welcome. Diversity of skills, perspectives and backgrounds is how we create, innovate, and disrupt like no other.

Final compensation

Final compensation will be based on the candidate's qualifications, skills, and experience. This is a general Engineer opening, not tied to one fixed team. Depending on need at the time of hiring, you could be placed in one of several domains. A domain is a group of teams working on a related set of problems — for example, everything to do with risk, or everything to do with checkout.