• Develop models that make every customer interaction more relevant and timely • Ensure they receive products and services tailored to their needs • Build scalable, explainable, and responsible AI solutions that enhance trust, transparency, and the overall customer experience • Provide technical leadership and shipping highly impactful ML-based solutions • Work closely with product managers, data scientists, backend engineers and designers in an agile environment • Steer technical work and drive up standards within the Machine Learning discipline • Identify and scope out the most impactful opportunities to tackle business problems in personalisation • Lead the design and development of advanced real-time Machine Learning models • Provide technical leadership to drive up levels of technical expertise and best practice across the Machine Learning discipline

• Play a key role in a multidisciplinary squad collaborating with Machine Learning Scientists, Data Scientists, and other specialists. • Detect suspicious user behaviours while minimizing impact on genuine customers and costs. • Adapt to changing fraud trends, ensuring detection systems remain performant. • Design global machine learning solutions.

• Delivery and technical leadership • Lead the architecture and hands-on implementation of end-to-end ML systems: data ingestion, pipelines, feature stores, training, evaluation, serving, and monitoring • Own technical decisions across the full stack, data platform, training environment, model serving, and MLOps tooling • Set engineering standards for ML projects: experiment tracking, model versioning, reproducibility, governance, observability, drift monitoring, and CI/CD for ML • Coach and uplift other engineers on the team in modern ML and MLOps practices • Stay accountable for quality, security, and operational soundness of what we ship • Pre-Sales and pipeline support • Partner with the sales leadership team across pre-sales activity: discovery calls, scoping workshops, technical briefings, and LOE preparation • Lead architecture and solutioning conversations with prospects and customers, translate business problems into credible, defensible technical approaches • Provide dedicated technical support to opportunities flowing through the partners sales process, including positioning their products as part of broader data and AI architectures, joint solutioning sessions, and partner-aligned proposals • Contribute to thought leadership and demand generation: blog posts, webinars, capability decks, conference talks, and reference architectures

Role 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. 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. 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 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. 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.