Research Scientist, Recommendation Systems
About Hook
Hook is a social music platform, backed by top-tier investors, building the next generation of music discovery, fan expression, and creator growth. We turn the songs people love into sounds they can play with — enabling fans and creators to reimagine music through intuitive, remix-friendly experiences with zero learning curve. By partnering directly with artists, labels, and rightsholders, Hook ensures music is discovered, shared, and expressed in ways that respect and reward the people behind it. At Hook, music isn’t just consumed — it’s experienced, expressed, and made personal.
Role Overview
We are looking for a Research Scientist to design, develop, and advance state-of-the-art recommendation systems that power personalized user experiences at scale. In this role, you will conduct applied research, translate theoretical insights into production-ready models, and partner closely with engineering and product to drive measurable impact.
You’ll work on problems such as ranking, personalization, user modeling, exploration-exploitation, and representation learning, using real-world data and user-generated content.
What You’ll Do
• Design, implement, and evaluate machine learning models for recommendation, ranking, and personalization
• Conduct applied research to improve model quality, robustness, and efficiency
• Develop and execute experimentation strategies using offline evaluation and online testing
• Work with complex datasets to understand user behavior and system performance
• Collaborate with engineering and product teams to transition research into production systems
• Share research results in clear, accessible ways with both technical and non-technical audiences
• Stay informed about advances in recommendation systems and related ML methodologies
Required Skills & Experience
• Advanced degree (PhD or MS) in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience
• Experience developing recommendation, ranking, or personalization models for user-generated content.
• Strong foundation in machine learning, statistics, and data analysis
• Proficiency in Python and modern ML frameworks (e.g., PyTorch)
• Ability to conduct independent research and collaborate effectively in team environments
Preferred Skills & Experience
• Prior publications or submissions to RecSys or closely related venues
• Experience working with large-scale data and computational systems
• Experience with representation learning, sequence modeling, or large-scale optimization
• Experience transitioning research prototypes into production systems
Why This Role Matters
This role will influence the company’s research direction—how we develop novel models, push technical boundaries, and bring cutting-edge science into production. You’ll have significant ownership, and the opportunity to build a world-class applied research function from the ground up.
Compensation
Competitive base salary, meaningful equity ownership, and the opportunity to build and lead growth at a fast-growing consumer company.
Hook is a social music platform, backed by top-tier investors, building the next generation of music discovery, fan expression, and creator growth. We turn the songs people love into sounds they can play with — enabling fans and creators to reimagine music through intuitive, remix-friendly experiences with zero learning curve. By partnering directly with artists, labels, and rightsholders, Hook ensures music is discovered, shared, and expressed in ways that respect and reward the people behind it. At Hook, music isn’t just consumed — it’s experienced, expressed, and made personal.
Role Overview
We are looking for a Research Scientist to design, develop, and advance state-of-the-art recommendation systems that power personalized user experiences at scale. In this role, you will conduct applied research, translate theoretical insights into production-ready models, and partner closely with engineering and product to drive measurable impact.
You’ll work on problems such as ranking, personalization, user modeling, exploration-exploitation, and representation learning, using real-world data and user-generated content.
What You’ll Do
• Design, implement, and evaluate machine learning models for recommendation, ranking, and personalization
• Conduct applied research to improve model quality, robustness, and efficiency
• Develop and execute experimentation strategies using offline evaluation and online testing
• Work with complex datasets to understand user behavior and system performance
• Collaborate with engineering and product teams to transition research into production systems
• Share research results in clear, accessible ways with both technical and non-technical audiences
• Stay informed about advances in recommendation systems and related ML methodologies
Required Skills & Experience
• Advanced degree (PhD or MS) in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience
• Experience developing recommendation, ranking, or personalization models for user-generated content.
• Strong foundation in machine learning, statistics, and data analysis
• Proficiency in Python and modern ML frameworks (e.g., PyTorch)
• Ability to conduct independent research and collaborate effectively in team environments
Preferred Skills & Experience
• Prior publications or submissions to RecSys or closely related venues
• Experience working with large-scale data and computational systems
• Experience with representation learning, sequence modeling, or large-scale optimization
• Experience transitioning research prototypes into production systems
Why This Role Matters
This role will influence the company’s research direction—how we develop novel models, push technical boundaries, and bring cutting-edge science into production. You’ll have significant ownership, and the opportunity to build a world-class applied research function from the ground up.
Compensation
Competitive base salary, meaningful equity ownership, and the opportunity to build and lead growth at a fast-growing consumer company.