Staff Backend Engineer, Recommender Systems

About the Role

Join Raya as a Staff Backend Engineer to architect and build our sophisticated recommendation ecosystem, bridging ML/AI with production backend engineering to deliver performant and intelligent algorithms.

Responsibilities

• Own the end-to-end architecture of Raya’s recommendation services while remaining hands-on in implementation.
• Design and ship systems handling cold-start problems, real-time user signals, exposure balancing, and large-scale feature lookups.
• Evolve ranking systems toward scalable multi-stage architectures, including embedding-based retrieval and graph-aware ranking.
• Act as the primary technical liaison between Data Science, Product, and Infrastructure, translating algorithmic requirements into scalable backend services.
• Elevate the engineering bar through deep-dive design reviews, establishing best practice standards, and mentoring Senior Engineers.
• Ensure the reliability of mission-critical recommendation loops, optimizing for low-latency inference and high-availability.
• Operate in evolving problem spaces balancing short-term engagement, long-term retention, and marketplace health.
• Partner with Product/Data Science to implement offline and online experiments.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent real-world expertise.
• 8+ years of software development experience, with at least 3 years focused on Recommender Systems in a production environment.
• Deep practical experience with recommender approaches like collaborative filtering, content-based filtering, and hybrid models; experience with two-stage architectures.
• Expert-level proficiency in Golang, Node.js, or Python; experience building or operating high-throughput discovery, search, or recommendation systems.
• Advanced knowledge of Postgres, MongoDB, and ElasticSearch/OpenSearch, specifically regarding performance tuning.
• History of shipping platforms that have scaled to millions of users; comfort discussing consistency, availability, and latency trade-offs.
• Experience designing and implementing A/B tests in marketplace or interference-prone environments.

What Sets You Apart

• Understanding of ranking people in environments where exposure affects behavior, designing with fairness, liquidity, and user perception in mind.
• Strong product judgment, protecting serendipity, privacy, and user trust while shipping measurable improvements.
• Ability to build durable internal abstractions, tooling, and documentation.
• Understanding of the math behind ranking models and ability to identify bias and feedback loops.
• Optimizing for shipping measurable impact over technical novelty; knowing when to apply simple heuristics versus complex models.
• Bias toward shipping quickly, learning from production signals, and iterating with discipline.