Senior Machine Learning Engineer, Recommendation

Sekai•San Francisco, CA

5d•Remote

About The Position

Sekai is building an AI-driven consumer platform for creating, remixing, sharing, and playing interactive mini apps. The Senior Machine Learning Engineer will own search and recommendation systems for Sekai’s consumer product. The role is central to content discovery, user engagement, and content distribution, building models and systems that determine what users see and search for, what they continue playing, and how content reaches the right audience. This is a senior individual-contributor role for someone with production experience building recommendation, search, ranking, or discovery systems for consumer apps, particularly entertainment-driven products. The engineer will develop and improve retrieval and ranking systems, run experiments, improve recommendations for new users and new or rapidly changing content, and build practical ML systems that move from prototype to production with monitoring and evaluation. The role also contributes to the long-term architecture for AI-native content discovery and involves close collaboration with product, data, and engineering teams.

Requirements

At least 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 with 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 methods, 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 and search experiments end-to-end, using results to iterate.
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 deliver measurable improvements to retention, engagement, and content distribution.
Build practical ML systems that move quickly from prototype to production, with clear monitoring and evaluation.
Help shape the long-term ML architecture for AI-native content discovery.