(Senior) Applied Scientist, Recommendations · Consumer Engineering

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.

Wolt is part of DoorDash - together we form one of the world’s largest local commerce platforms. We build recommendation systems that help customers discover the most relevant restaurants, dishes and items throughout their Wolt experience.

We are looking for an Applied Scientist to advance the machine learning models behind these experiences. You’ll work on challenging applied ML problems where model quality, product decisions and customer experience are tightly connected. This is an opportunity to take ideas from problem framing and data analysis through experimentation, production deployment and measurable customer impact.

What you’ll be doing

Design, develop and improve recommendation, ranking and retrieval models that surface relevant restaurants, dishes, items and content to customers.

Own applied ML problems end to end: frame the problem, analyze data, develop models, define offline evaluation, run experiments and monitor production performance.

Develop methods that balance relevance with product and customer needs, such as diversity, availability, business constraints and changing user intent.

Collaborate closely with Software Engineers, ML Engineers, Product Managers and Analysts to turn scientific insights into reliable customer-facing products.

Evaluate and apply state-of-the-art ML methods where they meaningfully improve recommendation quality, robustness or efficiency.

Contribute to a high bar for applied-science practice through technical reviews, knowledge sharing and thoughtful experimentation.

Our humble expectations

You have substantial hands-on experience applying machine learning to real-world problems and a track record of bringing models from development into production; a PhD with relevant applied research experience is equally welcome.

You have experience with recommendation systems, ranking, retrieval, personalisation, or closely related ML problems.

You can independently turn an ambiguous customer or product problem into a well-scoped ML approach, make sound trade-offs and drive it to a measurable outcome.

You are proficient in Python and experienced with modern ML frameworks and large-scale data processing.

You understand how to evaluate ML systems rigorously, including offline metrics, experiment design and interpreting online results.

You communicate complex technical ideas clearly and work effectively with cross-functional partners.

What we offer

You will work on recommendation problems with direct, measurable impact on how customers discover relevant content. You’ll collaborate with experienced scientists and engineers across DoorDash, Deliveroo and Wolt, learning from multiple recommendation systems while helping shape the next generation of the experience.

Together with your lead, you will have the opportunity to create a personalised development plan that builds on your strengths and develops new capabilities.

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.