Sekai
Technology
SeniorMachineLearningEngineer,Recommendation
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Machine Learning Engineer, Recommendation at Sekai. Skills: Recommendation systems, Search systems, Ranking, Content discovery. Build recommendation systems. Improve recommendation systems”
What You'll Achieve.
Ship measurable improvements
Industry & Context.
Translate user behavior
What They're Looking For.
Must Have
5+ years industry experience, Production ML systems experience, Recommendation systems experience, Search systems experience, Ranking systems experience, Ads ranking experience, Feed ranking experience, Content discovery systems experience, Consumer apps experience, Two-tower models experience, Embedding retrieval experience, Candidate generation experience, Ranking experience, Online evaluation experience, Offline evaluation experience, Modeling experience, Data pipelines experience, Backend integration experience, Experimentation experience, Production ML systems experience
Nice to Have
AI recommendation experience, LLM-powered ranking experience, Semantic search experience, Personalized generation experience, AI-native content understanding experience, UGC content ecosystems experience, Creator marketplaces experience, Rapidly changing content catalogs experience, Multimodal content understanding experience, Explore/exploit experience, Contextual bandits experience, Reinforcement learning experience, Long-term value optimization experience, Startup experience, 0-to-1 ML systems experience
What You'll Do.
Build recommendation systems
Improve recommendation systems
Improve search systems
Own retrieval systems
Design recommendation experiments
Launch recommendation experiments
Analyze recommendation experiments
Improve recommendation quality
Build user representations
Build content representations
Build creator representations
Build session representations
Partner with product teams
Partner with data teams
Partner with engineering teams
Ship measurable improvements
Build practical ML systems
Shape ML architecture
How You'll Work.
Team & Collaboration
Partner with product; Partner with data; Partner with engineering
Full Job Description
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, Mayfield, and A*, with $30M raised to date. We are remote-first across all positions and are looking for ambitious, AI-native builders who want to help shape the next generation of consumer social products. Here are Sekai’s download links: - Android: https://play.google.com/store/apps/details?id=chat.sekai.app&hl=en_US - iOS: https://apps.apple.com/us/app/sekai-create-remix-play/id6504506653 ABOUT THE ROLE 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. You will build the models and systems that decide what users see, what they search for, what they continue playing, and how high-quality content reaches the right audience. 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. 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
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