Senior Software Engineer, Feature Engineering Platform
Wolt
Salary undisclosed
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Description
About Wolt
At Wolt, we create technology that brings joy, simplicity and earnings to the neighborhoods of the world. In 2014 we started with delivery of restaurant food. Now we’re building the delivery of (almost) everything and you’ll find us in over 500 cities in 30 countries around the world. In 2022 we joined forces with DoorDash and together we keep on dreaming big and expanding across the globe.
Working at Wolt isn’t always easy, but it’s definitely exciting. Here you’ll learn more, build more, and ship more than in most other companies. You’ll be challenged a lot, but also have a lot of fun on the way. So, if you’re a self-starter with drive and entrepreneurial spirit, this could be the ride of your life.
About the Role
We’re building a new Feature Engineering Platform that will be the front door to ML development at global scale. As a Software Engineer on this team, you’ll help build a seamless experience for ML practitioners across the company — from experimentation and exploration to deployment and monitoring.
Our mission is to make machine learning accessible and intuitive without compromising on power or performance. You’ll work on a layered platform that supports both high-level declarative workflows and lower-level imperative APIs, all integrated into the environments our users already work in (like notebooks and internal sandboxes).
What You’ll Do
- Build and evolve APIs, SDKs, and platform integrations that power feature engineering workflows.
- Help shape user-facing tools to make the platform approachable for all skill levels.
- Collaborate across the ML lifecycle — from data ingestion to feature serving — and integrate tightly with our central Data Control Plane.
- Contribute to platform reliability, testing, observability, and operational excellence.
Qualifications
- Solid engineering background, ideally in platform, data, or ML-adjacent teams.
- Comfortable working with backend systems, APIs, and ideally some exposure to distributed compute (e.g. Spark, Flink).
- Empathy for users and interest in building abstractions that remove friction.
- Curious and collaborative mindset — eager to learn from others and contribute broadly.
- Experience with ML tooling, or data pipelines is a plus.
Our Commitment to Diversity and Inclusion
We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.
Beware of recruitment scams: DoorDash, Deliveroo, and Wolt will never ask you to pay money or share sensitive financial information during hiring — learn more about our legitimate recruiting process at Recruitment Scam Awareness - DoorDash .
About this role
Wolt is building a Feature Engineering Platform designed to streamline how machine learning practitioners across the company develop and deploy models at scale. As a Software Engineer on this team, you'd be responsible for creating the APIs, SDKs, and platform integrations that make feature engineering workflows intuitive and powerful—bridging the gap between high-level experimentation in notebooks and production-grade deployment and monitoring systems.
The role calls for someone with solid backend engineering experience, ideally from platform, data, or ML-adjacent work. You should be comfortable with distributed systems concepts (Spark, Flink) and have a genuine interest in designing abstractions that reduce friction for users across different skill levels. Prior exposure to ML tooling or data pipelines is valued but not required; what matters more is your ability to collaborate across the ML lifecycle, from data ingestion through feature serving, and your curiosity about how to make complex systems accessible.
This is an in-office position in Berlin, suited for someone who thrives in a fast-paced environment and enjoys shipping features at scale. You'd be working alongside teams focused on data infrastructure and ML operations, contributing to reliability, testing, and observability alongside feature development.
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Pay for this role
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