Machine Learning Engineer Personalisation

Wolts Personalization team is responsible for creating a tailored experience for Wolts customers across their shopping journey selecting the best restaurants dishes or items to match their culinary and shopping preferences across multiple premises such as Discovery In Venue or Checkout. The Personalization team owns the ML stack models and integrations that generate realtime recommendations for millions of customers across all Wolt markets. For example the team is responsible for the models that rank restaurants in Wolts Discovery and Restaurants tabs venues in Wolts Discovery and Stores tabs or items in Wolts invenue and cart premises. It also personalises other components in Wolts shopping experience such as brands banners or food categories that are relevant entry points for our customers to explore Wolts assortment.

As a Machine Learning (ML) Engineer in Wolts Personalization team you will :

Build the ML infrastructure to develop train and deploy Wolts ranking models that select the content to display to our customers;

Work endtoend from use case design to implementation delivery and monitoring of your solutions;

Maintain our production ML stack and raise the teams ML engineering excellence bar;

Liaise with Wolts ML Platform team to adopt different ML technologies and to create technical requirements for their solutions;

Contribute to Wolt ML Engineering and Applied Science communities;

Be part of a crossdisciplinary team with Applied Scientists Software Engineers and Analysts to provide solutions to customer problems with a direct impact on the companys business KPIs;

Work at Wolts scale : Wolt operates in 30 different markets with millions of customers.

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.  Read more about our remote setup here.

Qualifications :

You are experienced in endtoend machine learning deployments and maintenance of ML systems and have at least 2 years of experience in ML / MLOps;

You have deployed and ran ML models in production at scale maybe with hundreds of RPS and low latency;

You bring solid experience in scaling solutions monitoring ML stacks and troubleshooting ML deployments to the table. You can help with the technical issues the teams encounter;

You are experienced in implementing realtime inference ML models in production;

Good understanding of ML and MLOps principles as well as Software engineering experience in Python should complete your profile;

Experienced in Docker Kubernetes workflow orchestration tools (e.g. Flyte) model and experiment registries (e.g. MLflow) and model serving systems (e.g. Seldon);

You have solid communication and collaboration skills and are experienced in coordinating initiatives with your team and main stakeholders.

Additional Information :

The position will be filled as soon as we find the right person so make sure to apply as soon as you realize you really really want to join us!

The compensation will be a negotiable combination of monthly pay and DoorDash RSUs. The latter makes it exceptionally easy to be excited about our company growing and doing well as youll own a piece of the pie.

For any further questions about the position you can turn to Product Talent Acquisition Partner Fernanda Prado ().

Remote Work : Employment Type :

Fulltime