Senior Machine Learning Engineer, Recommendation
New
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SekaiConsumer social
Location: San Francisco, USAFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of industry experience
- Required Skills
- Machine Learning
Requirements
- Have 5+ years of industry experience building production ML systems.
- Have senior-level ownership of recommendation, search, ranking, ads ranking, feed ranking, or content discovery systems.
- Have hands-on experience building recommendation or search systems for consumer apps.
- Have experience with entertainment, social, gaming, short-form content, creator, or other engagement-driven consumer products.
- Have practical experience with two-tower models, embedding retrieval, candidate generation, ranking, and online/offline evaluation.
- Bring product intuition around relevance, retention, engagement, satisfaction, cold start, and content distribution.
- Translate messy user behavior into useful modeling signals and practical product improvements.
- Have engineering fundamentals across modeling, data pipelines, backend integration, experimentation, and production ML systems.
- Demonstrate ownership, fast execution, and clear communication in ambiguous product environments.
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, then use the data 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 ship improvements to retention, engagement, and content distribution.
- Build practical ML systems that move from prototype to production, with monitoring and evaluation.
- Help shape the long-term ML architecture for AI-native content discovery.
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