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This position was posted on March 21, 2026 and is likely no longer accepting applications. We've kept it here for historical reference. Check out the similar jobs below!
Job Overview
Location
Remote
Job Type
Full-time
Category
Data Science
Date Posted
March 21, 2026
Full Job Description
đź“‹ Description
- • As a Research Scientist in Recommendation Systems at Hook Music, Inc., you will design, develop, and advance state-of-the-art recommendation systems that power personalized user experiences at scale, directly influencing how fans and creators interact with music through intuitive, remix-friendly experiences.
- • Your work will translate theoretical insights into production-ready models, driving measurable impact on user engagement and music discovery by partnering with engineering and product teams to bring cutting-edge research into real-world applications.
- • You will design, implement, and evaluate machine learning models for recommendation, ranking, and personalization using real-world data and user-generated content, focusing on problems such as ranking, personalization, user modeling, exploration-exploitation, and representation learning.
- • You will conduct applied research to improve model quality, robustness, and efficiency, developing and executing experimentation strategies using both offline evaluation and online testing to validate innovations before deployment.
- • You will work with complex datasets to understand user behavior and system performance, leveraging large-scale data to uncover patterns that inform model improvements and product decisions.
- • You will collaborate closely with engineering and product teams to transition research prototypes into production systems, ensuring seamless integration of scientific advancements into the platform’s core functionality.
- • You will share research results in clear, accessible ways with both technical and non-technical audiences, fostering alignment across teams and promoting a culture of data-driven decision-making.
- • You will stay informed about advances in recommendation systems and related ML methodologies, continuously integrating state-of-the-art techniques to keep Hook at the forefront of music tech innovation.
- • You will have significant ownership in shaping the company’s research direction, with the opportunity to build a world-class applied research function from the ground up, influencing long-term technical strategy and innovation.
- • You will contribute to a mission-driven culture where music is not just consumed but experienced, expressed, and made personal, working alongside artists, labels, and rightsholders to ensure ethical and rewarding music discovery.
🎯 Requirements
- • 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
🏖️ Benefits
- • Competitive base salary
- • Meaningful equity ownership
- • Opportunity to build and lead growth at a fast-growing consumer company
- • Remote work flexibility
- • Chance to work on mission-driven product that transforms how music is experienced and expressed
Skills & Technologies
About Hook Music, Inc.
Hook Music is a technology company that provides AI-powered music creation tools for content creators and businesses. Their platform enables users to generate unique, royalty-free music tailored to specific needs, such as background scores for videos, podcasts, or marketing campaigns. By leveraging advanced artificial intelligence, Hook Music democratizes music production, allowing individuals without musical expertise to create professional-sounding tracks. They operate within the rapidly growing creator economy and digital media industries, offering a subscription-based service that provides access to their extensive music library and customization features. Their focus is on delivering high-quality, adaptable audio solutions efficiently and affordably.
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