Senior Machine Learning Engineer, Recommendation

Sekai is building an AI-driven consumer platform for interactive content and is seeking a Senior Machine Learning Engineer to own search and recommendation systems for their product. The role involves building models and systems for content discovery, user engagement, and content distribution, focusing on improving recommendation quality and collaborating with product, data, and engineering teams.

Responsibilities:

• 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

Requirements:

• 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
• 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