Senior ML Engineer – Remote-First AI Discovery

Sekai; United States; Remote; USD 150,000 - 210,000; Full time

Benefits offered by this job

Competitive salary

Equity

Health insurance

Job summary

Sekai is an AI-native consumer platform that enables people to create, remix, share, and play interactive content. We are remote-first and backed by strong investors, building the next generation of AI-driven social apps.

The Senior Machine Learning Engineer role focuses on search and recommendation, shaping content discovery and user engagement. You will own models and systems that decide what users see, search for, and continue watching, driving quality recommendations at scale.

Qualifications

• 5+ years of experience building production ML systems for consumer apps.
• Hands-on experience with recommendation or search systems in production.
• Experience with entertainment, social, gaming, or other engagement-driven products.

Responsibilities

• Build and improve recommendation and search systems across feed, discovery, search, and content surfaces.
• Own retrieval and ranking systems, including candidate generation and online serving quality.
• Design, launch, and analyze experiments end-to-end to iteratively improve metrics.
• Improve quality for new users, new content, and dynamic content pools.
• Develop representations from behavioral signals at user/content/creator/session levels.
• Collaborate with product, data, and eng teams to define metrics and ship improvements.

Skills

Production ML

Recommendation

Search systems

Two-tower models

Embedding retrieval

Online/offline eval

Job description

Sekai is an AI-native consumer platform that enables people to create, remix, share, and play interactive content. We are remote-first and backed by strong investors, building the next generation of AI-driven social apps.

The Senior Machine Learning Engineer role focuses on search and recommendation, shaping content discovery and user engagement. You will own models and systems that decide what users see, search for, and continue watching, driving quality recommendations at scale.