• Architectural Leadership: Own the end-to-end architecture of Raya’s recommendation services while remaining deeply hands-on in implementation. • Hands-on Implementation: Design and ship systems that handle cold-start problems, real-time user signals, exposure balancing, and large-scale feature lookups. • System Evolution: Evolve our ranking systems toward scalable multi-stage architectures, including embedding-based retrieval and graph-aware ranking where appropriate. • Cross-Functional Influence: Act as the primary technical liaison between Data Science, Product, and Infrastructure. Translate complex algorithmic requirements into scalable backend services. • Mentorship & Excellence: Elevate the engineering bar across the organization. Conduct deep-dive design reviews, establishing best practice standards for backend patterns, and mentor Senior Engineers in recommender systems best practices. • Operational Stewardship: Ensure the reliability of mission-critical recommendation loops. Optimize for low-latency inference and high-availability, even during peak global traffic. • Ambiguity & Tradeoffs: Operate in evolving problem spaces where objectives must balance short-term engagement, long-term retention, and marketplace health. • Experimentation: Partner with Product/Data Science to implement offline + online experiments.