Recommendation Systems Engineer

Role overview The Recommendation Systems Engineer builds the personalization infrastructure behind a consumer game feed for a platform where creators publish user-generated games and players discover them through recommendations. It is an end-to-end engineering role covering data foundations, candidate generation, ranking, and online serving. The position is well suited to an engineer who enjoys taking systems from an early rules-based stage to mature machine-learning models and who cares about both modeling quality and production performance.

Responsibilities - Define the events, metadata, and behavioral signals needed to understand users and games. - Design data models and pipelines that transform raw activity into reliable recommendation features. - Build and maintain user profiles, game profiles, and user-game interaction datasets. - Develop candidate-generation and ranking approaches, starting with practical systems and increasing sophistication over time. - Productionize recommendation models and integrate them into the feed infrastructure. - Build efficient online-serving and caching strategies that keep the feed fast and scalable. - Establish offline evaluation, online experimentation, monitoring, and model-performance reporting. - Tackle cold-start, content discovery, diversity, and creator-distribution problems.

Requirements - Experience designing or building recommendation, ranking, search, ML, backend, or data systems end to end, including data collection, feature development, modeling, production serving, and evaluation. - Background in machine learning or applied ML with strong Python and SQL skills. - Familiarity with technologies such as Redis, Kafka, Spark, OpenSearch, or Elasticsearch. - Experience with vector search, approximate nearest-neighbor retrieval, or feature-store infrastructure. - Experience handling cold-start, sparse interaction data, and rapidly changing content catalogs. - Track record of taking recommendation systems from an early rules-based stage to a mature ML system. - Strong interest in games, creator ecosystems, and interactive content.

Benefits and work setup - Remote, full-time engineering position. - Small, venture-backed team with an organic catalog of tens of thousands of user-generated games.