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# Senior Engineering Manager, Recommender Systems

- **Company:** Raya App, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Amazon Web Services, Distributed Systems, Elasticsearch, Java Virtual Machine (JVM), PostgreSQL, Machine Learning, Recommender Systems, Search Technologies, Feature Store, Backend, Kubernetes, OpenSearch
- **Published:** November 7, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e7a0776be2e6f05e

## About the Role

We are looking for someone who is technically strong, has deep experience with production recommender systems, and has intuitive product-focused user empathy., * 3+ years managing engineers, with direct responsibility for hiring, performance management, career development, and delivery.

 * Experience leading or building production recommender systems at scale.
 * Hands-on depth in high-throughput, low-latency distributed systems that serve millions of users.
 * Expertise in Go, Java/JVM, or a similar backend language; Postgres, and AWS/GCP, and Kubernetes., * Experience with social networks or two-sided marketplaces in a subscription setting: you understand reciprocal interest, exposure, liquidity, and how great recommendations create long-term retentive value.
 * Familiarity with how machine learning and AI fit into modern recommender systems.
 * Experience with retrieval infrastructure such as Elasticsearch/OpenSearch, vector search, or feature stores.

## Description

* Build, coach, and retain a high-performing backend team that specializes in recommender systems.
 * Own reliable, low-latency production systems, observability, and incident response for recommendations. Catch regressions in latency, quality, or fairness before users notice.
 * Create and execute a technical strategy that is grounded in deep product intuition and empathy for the end-user experience.
 * Partner with the Machine Learning team to define roadmaps. Balance user-facing improvements with investments in infrastructure, reliability, and experimentation.
 * Work with Data Science to co-own experimentation processes, tools, and best practices.