Research Scientist, Recommendation Systems
Confirmed on the employer's own hiring board on Nov 14, 2026. First seen by Alion on Nov 10, 2026.
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.
Responsibilities
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
Qualifications
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
Bonus Qualifications
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
What We Offer
Opportunity to shape product direction through data and cutting-edge research
Freedom to shape research direction and problem selection
Work closely with teammates across research, engineering, and product as the organization grows.
Support for continued learning and professional growth
Competitive compensation and benefits