Senior Machine Learning Engineer, Recommendation

Role overview Own the machine learning systems that help users discover, search, and continue engaging with relevant interactive content. This senior individual contributor role covers retrieval, ranking, experimentation, and production delivery across feed and discovery experiences, with a focus on measurable improvements to user and content matching.

Responsibilities - Build and improve recommendation and search systems for feeds, discovery, search, and content continuation. - Develop candidate generation, embedding-based retrieval, two-tower models, ranking features, and online serving. - Plan and analyze experiments end to end, then use results to refine system quality. - Improve recommendations for new users and new content as the catalog and user interests change. - Create user, creator, content, and session representations from behavioral signals. - Work with product, data, and engineering partners on metrics, evaluation, monitoring, and production launches.

Requirements - Five or more years building production machine learning systems, including senior ownership of recommendation, search, ranking, or content discovery. - Practical experience delivering recommendation or search systems in consumer applications, ideally in entertainment, social, gaming, or other engagement-driven products. - Strong command of embedding retrieval, candidate generation, two-tower approaches, ranking, and online and offline evaluation. - Product judgment around relevance, retention, engagement, satisfaction, cold start, and content distribution. - Ability to turn noisy user behavior into useful modeling signals and product improvements. - Solid engineering across modeling, data pipelines, backend integration, experimentation, and production ML; clear communication and ownership in ambiguous settings.

Nice to have - Experience with LLM-powered ranking, semantic search, personalized generation, or AI-based content understanding. - Familiarity with user-generated content, creator marketplaces, multimodal signals, contextual bandits, reinforcement learning, or long-term value optimization. - Experience building early-stage ML systems in a startup or with limited infrastructure.

Benefits and work setup The package includes competitive compensation, meaningful equity, health insurance, and other benefits.