Applied Scientist / Machine Learning Engineer

Wolt is part of DoorDash — together we form one of the world's largest local commerce platforms, operating across DoorDash, Deliveroo, and Wolt markets in 40+ countries. Our Consumer organisation sits at the intersection of machine learning and customer experience, responsible for helping millions of customers every day find the right restaurants, dishes, and items in search, personalised recommendations, and discovery surfaces that feel intuitive and relevant. It's a domain where scale is truly global, the engineering and applied science problems are genuinely hard and scientifically interesting, and the impact on the customer experience and our business metrics is immediate and measurable.

We’re looking for an Applied Scientist to join our Consumer org. In this role you’ll work on some of the most technically challenging ML problems across DoorDash: understanding what our customers are looking for, identifying key concepts in their queries and surfacing the most relevant results. You'll be embedded in a cross-disciplinary team of engineers, ML engineers and applied scientists with full ownership from research to production. If your expertise matches our domain and you want to work on hard problems at global scale alongside exceptional colleagues, we'd love to meet you.

What you’ll be doing

As an Applied Scientist in Consumer, you’ll advance the ML models and methods that power how customers across DoorDash, Deliveroo, and Wolt find and discover content. You’ll work end-to-end collaborating closely with engineers and product managers to deliver real impact.

Day-to-day in this role you’ll:

• Design and develop ML models for search relevance, query understanding, and ranking that operate across DoorDash, Deliveroo, and Wolt’s 40+ markets.
• Bring state-of-the-art solutions to our stack for the delight of our customers, helping the team to impact business metrics.
• Work end-to-end on ML problems: from problem framing and data analysis through model development, offline evaluation, and production monitoring.
• Collaborate with Software Engineers, ML Engineers, Product Managers, and Analysts to translate research insights into real customer impact.
• Contribute to our group-wide Applied Science community through knowledge sharing, technical reviews, and raising the bar on ML practices.

Our humble expectations* 

• You have 4+ years of hands-on experience in applied ML with a track record of shipping models to production (a PhD in ML with applied research experience is equally welcome).
• You have solid experience in Search: query understanding, query intent prediction, or semantic search.
• You are proficient in Python and experienced with ML frameworks and large-scale data processing.
• You communicate complex technical ideas clearly and collaborate effectively with cross-functional teams.
• Experience with NLP, dense retrieval, learning-to-rank, or embedding-based methods is a strong plus.

What we offer

In this role, you will have a direct and measurable impact on millions of customers every day. You’ll join a team of world-class applied scientists and engineers in DoorDash, Deliveroo and 

Wolt who care about both rigour and craft, tackling problems that are genuinely hard and impactful in ways that few companies can offer. Together with your lead, you will have the opportunity to create a personalised development plan to grow your strengths and to develop new capabilities.

📍 This role can be based in one of our tech hubs in Berlin, Helsinki, or Stockholm, or you can work remotely anywhere in Finland, Sweden, Germany. We also offer relocation support to help you join us.

Next steps*

Once you apply, you will go through 4 steps in our hiring pipeline:

• TA Screen: a 30-minute introductory call with one of our Talent Partners to learn about your background and tell you more about the role.
• Hiring Manager interview: a conversation covering your domain experience in Search or Personalisation, past projects, AI fluency, and ways of working. We will tell you more about the team, use cases, our tech stack, etc. and it will be your opportunity to ask questions.
• Coding & System Design: a technical session focused on ML system design and problem-solving with some of the team’s ML Engineers.
• Project / Expertise Deep Dive: an interview with the team’s Applied Scientists, going deep on your domain expertise and past projects.