Senior Machine Learning Engineer, Recommendation

What You Will Own

• Build and improve recommendation and search systems across feed, discovery, search, and content continuation surfaces.
• Own retrieval and ranking systems, including candidate generation, embedding-based retrieval, two-tower models, ranking features, and online serving quality.
• Design, launch, and analyze recommendation/search experiments end-to-end, then use the data to iterate quickly.
• Improve recommendation quality for new users, new content, and fast-changing content pools.
• Build user, content, creator, and session-level representations from behavioral signals.
• Partner with product, data, and engineering teams to define metrics, run experiments, and ship measurable improvements to retention, engagement, and content distribution.
• Build practical ML systems that can move from prototype to production quickly, with clear monitoring and evaluation.
• Help shape the long-term ML architecture for AI-native content discovery.

What We Are Looking For

• 5+ years of industry experience building production ML systems, with senior-level ownership of recommendation, search, ranking, ads ranking, feed ranking, or content discovery systems.
• Hands-on experience building recommendation or search systems for consumer apps.
• Experience working on entertainment, social, gaming, short-form content, creator, or other engagement-driven consumer products.
• Strong practical experience with two-tower models, embedding retrieval, candidate generation, ranking, and online/offline evaluation.
• Strong product intuition around relevance, retention, engagement, satisfaction, cold start, and content distribution.
• Ability to translate messy user behavior into useful modeling signals and practical product improvements.
• Strong engineering fundamentals across modeling, data pipelines, backend integration, experimentation, and production ML systems.
• High ownership, fast execution, and clear communication in ambiguous product environments.

Nice to Have

• Experience with AI recommendation, LLM-powered ranking, semantic search, personalized generation, or AI-native content understanding.
• Experience with UGC content ecosystems, creator marketplaces, or rapidly changing content catalogs.
• Experience with multimodal content understanding across text, image, video, interaction traces, or generated content.
• Experience with explore/exploit, contextual bandits, reinforcement learning, or long-term value optimization.
• Startup experience or experience building 0-to-1 ML systems with limited infrastructure.