Research Scientist, AI Music

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, AI Music to advance the state of the art in AI-powered music creation, transformation, and interaction. In this role, you will research, develop, and evaluate machine learning models that enable new ways for people to play with music, while translating cutting-edge research into intuitive, high-quality product experiences.

You’ll work at the intersection of music, machine learning, and creative technology, collaborating closely with our music technology team, producers, and product and engineering partners to turn creative ideas into powerful AI tools. From exploring generative audio models and music understanding to developing novel ways to remix, transform, and interact with music, you’ll help define how AI can make musical expression more accessible.

At Hook, we believe the future of AI music should be built responsibly. We are committed to developing tools that respect artists, their creative work, and the rights of music creators and rightsholders. You’ll help shape this approach by building AI systems that enable creative expression while prioritizing ethical data practices, transparency, and responsible use of music.

RESPONSIBILITIES

- Research and develop AI music models: Design and develop machine learning models for music generation, transformation, remixing, and creative interaction.

- Explore emerging technologies: Investigate advances in generative audio, music understanding, audio representation learning, and related areas of machine learning.

- Translate research into products: Turn research ideas and prototypes into practical, high-quality AI capabilities, working closely with engineering and music technology teams.

- Experiment and evaluate: Design experiments and evaluation frameworks to assess audio quality, musicality, controllability, performance, and creative usefulness.

- Collaborate with creative teams: Work closely with producers, musicians, and cross-functional teams to understand creative workflows and develop intuitive, musically meaningful tools.

- Champion responsible AI development: Consider music rights, training data provenance, artist consent, and attribution throughout model development.

QUALIFICATIONS

- Advanced degree (PhD or MS) in Computer Science, Machine Learning, Audio Signal Processing, Music Technology, or a related field, or equivalent practical experience.

- Strong foundation in machine learning, deep learning, and statistical methods, with experience developing and evaluating ML models.

- Experience with audio or music-related machine learning, such as generative audio, music understanding, audio representation learning, source separation, or signal processing.

- Proficiency in Python and modern ML frameworks, such as PyTorch.

- Familiarity with audio processing libraries, datasets, and evaluation techniques.

- Ability to conduct independent applied research and translate research insights into working prototypes and real-world applications.

- Strong collaboration and communication skills, with the ability to work effectively across technical and creative disciplines.

- Genuine interest in music, creative tools, and the ways technology can expand musical expression.

BONUS QUALIFICATIONS

- Experience training, fine-tuning, or deploying generative audio or music models, including diffusion-based,