As a Staff Backend Engineer at Raya, you will be the technical architect and hands-on builder for our recommendation ecosystem, building and evolving sophisticated, multi-stage retrieval and ranking systems, bridging applied ML/AI with production backend engineering to deliver algorithms that are both performant and intelligent.
Requirements
- 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.
- Experimentation: Partner with Product/Data Science to implement offline + online experiments.
- 8+ years of software development experience, with at least 3 years focused specifically 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 (Candidate Generation & Ranking).
- Expert-level proficiency in Golang, Node.js, or Python. Experience building or operating high-throughput discovery, search, or recommendation systems in production.
- Advanced knowledge of Postgres, MongoDB, and ElasticSearch/OpenSearch, specifically regarding performance tuning for high-concurrency discovery features.
- 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.
- Experience designing and implementing A/B tests in marketplace or interference-prone environments.
