Senior Machine Learning Engineer, Recommendation
New
J
JobgetherConsumer entertainment
US; based in the United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- At least 5 years of industry experience developing and deploying production machine learning systems
- Required Skills
- Machine Learning
Requirements
- Have at least 5 years of industry experience developing and deploying production machine learning systems.
- Bring senior-level ownership of recommendation, search, ranking, advertising ranking, feed ranking, or content discovery.
- Have hands-on experience building recommendation or search systems for consumer-facing applications.
- Have experience with entertainment, social, gaming, short-form content, creator platforms, or other engagement-driven consumer products.
- Demonstrate practical expertise in two-tower models, embedding-based retrieval, candidate generation, and ranking algorithms.
- Have experience with offline and online model evaluation.
- Understand relevance, engagement, retention, user satisfaction, cold-start problems, and content distribution.
- Be able to turn complex behavioral data into modeling signals and measurable product improvements.
- Have engineering fundamentals spanning machine learning development, data pipelines, backend integration, experimentation frameworks, and production deployment.
- Demonstrate ownership, rapid execution, analytical thinking, and clear communication in ambiguous product environments.
- Collaborate across technical and product teams while independently driving complex projects from concept to launch.
- Preferred: Experience with AI-powered recommendations, LLM-based ranking, semantic search, personalized content generation, or AI-native content understanding.
Responsibilities
- Build, optimize, and maintain recommendation and search systems across personalized feeds, discovery pages, search experiences, and content continuation features.
- Own retrieval and ranking pipelines, including candidate generation, embedding-based retrieval, two-tower models, ranking features, and online serving performance.
- Design, launch, and analyze end-to-end recommendation and search experiments.
- Improve discovery for new users and newly published content by addressing cold-start challenges and changing user preferences.
- Develop user, content, creator, and session representations using behavioral signals and other relevant data sources.
- Partner with product, data, and engineering teams to establish success metrics, conduct experiments, and improve user engagement, retention, satisfaction, and content distribution.
- Build machine learning solutions from prototypes to production, with evaluation, monitoring, and performance optimization.
- Translate user behavior and product challenges into modeling strategies and actionable recommendations.
- Contribute to the architecture and technical direction of AI-powered content discovery systems.
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