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, autoregressive, or other neural audio architectures.
Experience with audio codecs, neural vocoders, audio tokenization, text-audio models, or multimodal learning.
Familiarity with agent frameworks and workflow orchestration tools for building multi-step AI applications.
Familiarity with music production workflows, DAWs, audio plugins, or creative software.
Experience collaborating directly with producers, musicians, or other creative professionals to develop or evaluate AI-powered tools.
Experience building low-latency, interactive audio systems or deploying ML models in production environments.
Experience designing evaluation methods for subjective qualities such as musicality, expressiveness, controllability, and creative utility.
Knowledge of responsible AI practices for music, including rights-aware dataset development, artist consent, data provenance, and attribution.
Research publications or contributions in machine learning, audio processing, generative AI, or music information retrieval.
Our Approach to Responsible AI Music
At Hook, we believe AI should expand what people can do with music while respecting the people who create it. We build with artists and rightsholders in mind, and we aim to develop AI tools that support creative expression rather than undermine the value of human creativity.
This means taking a thoughtful approach to the music and data used in model development, respecting rights and permissions, and considering how our technology affects artists, producers, and the broader music ecosystem. We value researchers who bring both technical ambition and a strong sense of responsibility to their work, and who see ethical considerations as an integral part of building great products.
What We Offer
The opportunity to shape the future of AI-powered music creation and expression.
Freedom to explore ambitious research ideas and influence technical and product direction.
Close collaboration with a multidisciplinary team of researchers, engineers, music technologists, and producers.
The opportunity to see your research become real tools used in creative workflows.
A mission-driven environment focused on making music more interactive, accessible, and expressive while respecting the people behind it.
Support for continued learning, research, and professional growth.
Competitive compensation and benefits.