Senior Machine Learning Engineer, Recommendation

Sekai is building the TikTok for mini apps — an AI-driven consumer platform where people create, remix, share, and play interactive content instantly. We are a Series A company backed by Khosla, a16z and Mayfield, with $30M raised to date. We are remote-first across all positions. We are looking for a Senior Machine Learning Engineer to own search and recommendation systems for Sekai's consumer product. This role sits at the center of content discovery, user engagement, and content distribution. This is a senior IC role for someone who has built real recommendation, search, ranking, or discovery systems in production for consumer apps, especially in entertainment-driven products.

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; 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

Benefits

Top compensation package, including competitive salary and meaningful equity; Comprehensive health insurance and benefits