Senior Machine Learning Engineer, Recommendation

Sekai

1d•Remote

About The Position

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. 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.

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.

Nice To Haves

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.

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.