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.

Education: Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent real-world expertise building and operating production recommendation or ranking systems.
Experience: 8+ years of software development experience, with at least 3 years focused specifically on Recommender Systems in a production environment.
RecSys Mastery: Deep practical experience with recommender approaches like collaborative filtering, content-based filtering, and hybrid models. Experience with two-stage architectures (Candidate Generation & Ranking).
Infrastructure Skills: Expert-level proficiency in Golang, Node.js, or Python. Experience building or operating high-throughput discovery, search, or recommendation systems in production.
Data Fluency: Advanced knowledge of Postgres, MongoDB, and ElasticSearch/OpenSearch, specifically regarding performance tuning for high-concurrency discovery features.
System Design: A history of shipping platforms that have scaled to millions of users. You should be comfortable discussing the trade-offs between consistency, availability, and latency.
A/B Testing: Experience designing and implementing A/B tests in marketplace or interference-prone environments.

Staff Backend Engineer, Recommender Systems

Key skills