Delivery Hero is one of the world leaders in online ordering and fast delivery
Search functionality spans more than 60 countries and 35 languages, facilitating over 80 million searches daily across four continents
Responsibilities
Own the Ranking Stack End-to-End: Design, build, and productionalize deep neural ranking models — including DCN-V2, MMoE, and Two-Tower architectures — operating at high throughput and low latency in production
Drive Agentic ML Research: Leverage LLM coding agents (e.g., Claude Code, Gemini) to autonomously iterate on feature engineering and Learning to Rank (LTR) architectures
Lead Feature Engineering and Model Architecture Innovation: Apply deep expertise in ranking signals, feature stores, and embedding-based retrieval
Ensure Production Reliability: Maintain rigorous standards for model health in production, own monitoring for model drift, latency degradation, and feature pipeline integrity
Collaborate Across Disciplines: Work as a technical partner with Backend Engineers, Data Engineers, and Product Managers
Raise the Bar: Mentor junior and mid data scientists, drive best practices in experimentation rigor and code quality
Requirements
Master's degree (or Bachelor's with 6+ years of work experience) in Computer Science, Mathematics, Physics, or a related quantitative field
4+ years of industry experience as a Data Scientist or Machine Learning Engineer applying ML in high-traffic production environments
Hands-on, production-proven experience implementing modern deep learning ranking architectures — DCN-V2, MMoE, Two-Tower
Strong command of multi-task learning, cross-feature interactions, and embedding optimization at scale
Solid foundation in LTR methods (pointwise, pairwise, listwise), offline evaluation metrics (NDCG, MRR)
Proficiency with LLM coding agents and agentic CLI tools (e.g., Claude Code, Gemini)