$ cat jobs/engineer-ii-ml-go-consumer-global-discovery-deliveryhero-78faf348d15f.json
Engineer II, ML/Go - (Consumer, Global Discovery)
As the world’s pioneering local delivery platform, our mission is to deliver an amazing experience, fast, easy, and to your door. We operate in around 65 countries worldwide, powered by tech, designed by people. As one of Europe’s largest tech platforms, headquartered in Berlin, Germany, Delivery Hero has been listed on the Frankfurt Stock Exchange since 2017 and is part of the MDAX stock market index. We push hard, learn quickly and stay human along the way. It’s this wonderful mix of high performance and real community that makes Delivery Hero a place where ambition and belonging grow side by side. If you’re curious, collaborative and ready to dive deep into meaningful work, you’ll fit right in. We are on the lookout for a Engineer II for our Core Recommendations Engineering squads. This role is critical to building and operating the systems that power personalized discovery at scale. You will own production services written in Go and Python that serve real-time recommendations across multiple surfaces, integrating tightly with machine-learning models and experimentation platforms. You would be responsible for delivering, enhancing and maintaining the recommendation system which is robust, scalable and observable. You will work closely with other engineers in the team on mainly backend engineering and few front-end related components. You would collaborate with data engineering, data science, product partners to translate the recommendation strategies and models into production-grade engineering solutions. Your squad is responsible for one of the foundational components of our customer discovery experience. End-to-End Recommendation System: Design, build, and operate the full recommendation flow from candidate retrieval (recall-oriented systems, embeddings, and filters), through ranking (feature enrichment, model inference, scoring), to re-ranking (business rules, diversity, freshness, and policy constraints). Ensure these pipelines meet strict latency, availabili
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