Machine Learning Engineer, Personalisation
Berlin, Germany
Wolt
When people try Wolt, they love it! The joy of food, groceries and more from local stores and restaurants, delivered in 30 minutes. Try the most-loved delivery app now.Company Description
Wolt is a Helsinki-based technology company that provides an online platform for consumers, merchants and couriers. It connects people looking to order food and other goods with people interested in selling and delivering them. To enable this, Wolt develops a wide range of technologies from local logistics to retail software and financial solutions – as well as operating its own grocery stores under the Wolt Market brand. Wolt’s products include Wolt+ (subscription service for customers), Wolt for Work (meal benefits and office deliveries for companies), Wolt Drive (fast last-mile deliveries for merchants) and Wolt Self-Delivery (service for merchant partners with their own delivery staff). Wolt’s mission is to make cities better by empowering and growing local communities. Wolt was founded in 2014 and joined forces with DoorDash in 2022. DoorDash operates in 29 countries today, 25 of which are with the Wolt product and brand.
Working in Product Development at Wolt
At Wolt, we’re about getting things done. You’ll probably enjoy it here if you like taking ownership, developing yourself and being around friendly, humble and ambitious people.
The behind the scenes of Wolt is run by an awesome bunch of over 700+ planners, builders, designers and data crunchers. We call ourselves Product+, as we’re the very core of Wolt’s products, tools and platforms. To build our products, we work in over 60 cross-functional, independent and autonomous teams. Teams are made up of a mix of talented individuals: engineers, designers, data scientists, analysts, and product leads. Each team takes ownership for solving customer problems in the best possible way.
Our Commitment to Diversity, Equity & Inclusion
We want to have all sorts of people in our team – people like you and me, and people different from you and me. To be able to work with diverse teammates – when it comes to gender, age, ethnicity, life background, sexual orientation, political views, religion, or any other personal trait – we consciously aim to offer equal opportunity for everyone to work with us. This is because we believe diverse teams make the most thought-through decisions and build things in the most inclusive way.
Join us today to build Wolt together.
Job Description
Wolt’s Personalization team is responsible for creating a tailored experience for Wolt’s 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 real-time recommendations for millions of customers across all Wolt markets. For example, the team is responsible for the models that rank restaurants in Wolt’s Discovery and Restaurants tabs, venues in Wolt’s Discovery and Stores tabs or items in Wolt’s in-venue and cart premises. It also personalises other components in Wolt’s shopping experience such as brands, banners or food categories that are relevant entry points for our customers to explore Wolt’s assortment.
As a Machine Learning (ML) Engineer in Wolt’s Personalization team you will:
Build the ML infrastructure to develop, train and deploy Wolt’s ranking models that select the content to display to our customers;
Work end-to-end, from use case design to implementation, delivery and monitoring of your solutions;
Maintain our production ML stack and raise the team’s ML engineering excellence bar;
Liaise with Wolt’s 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 cross-disciplinary team with Applied Scientists, Software Engineers and Analysts to provide solutions to customer problems with a direct impact on the company’s business KPIs;
Work at Wolt’s 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 end-to-end 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 real-time 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 you’ll own a piece of the pie.
For any further questions about the position, you can turn to Product+ Talent Acquisition Partner - Fernanda Prado (fernanda.prado.e.silva@wolt.com).
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Docker Engineering KPIs Kubernetes Machine Learning MLFlow ML infrastructure ML models MLOps Python Seldon
Perks/benefits: Career development Equity / stock options
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