AI Music Research Intern

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.

We are looking for a curious and motivated AI Music Research Intern to help us explore and develop AI-powered tools for music creation, transformation, remixing, and creative interaction.

In this role, you’ll work at the intersection of music, machine learning, and creative technology, experimenting with emerging AI techniques and helping turn research ideas into practical tools for music creators. You’ll have the opportunity to work on real-world challenges involving generative audio, music understanding, and audio modeling, while collaborating with our music technology, engineering, product, and creative teams.

Whether you have experience through academic research, coursework, or personal projects, this is an opportunity to apply your technical skills to exciting problems in AI music and help shape new ways for people to interact with music.

At Hook, we believe AI should expand creative possibilities while respecting the people behind the music. You’ll contribute to developing tools that support musical expression and responsible use of music and data.

• Explore AI music technologies: Research and experiment with machine learning approaches for music generation, transformation, remixing, and audio understanding.

• Implement and evaluate models: Use Python and modern ML frameworks to build, test, and evaluate AI models and audio processing techniques.

• Build prototypes: Develop experimental tools and prototypes that explore new ways to make music more interactive, expressive, and accessible.

• Work with data and experiments: Process audio datasets, run experiments, and evaluate model performance, audio quality, and musicality.

• Collaborate and share findings: Work with engineers, music technologists, and producers to explore creative use cases, document experiments, and contribute to responsible AI development.

• Currently pursuing a Bachelor’s, Master’s, or PhD degree in Computer Science, Machine Learning, Audio Signal Processing, Music Technology, Electrical Engineering, or a related field.

• Foundational knowledge of machine learning, deep learning, and statistical methods.

• Proficiency in Python and familiarity with machine learning frameworks such as PyTorch.

• Interest in audio, music, generative AI, or creative applications of machine learning.

• Familiarity with audio processing, digital signal processing, or music-related datasets through coursework, research, or personal projects.

• Strong analytical and problem-solving skills, with curiosity and a willingness to experiment with new ideas.

• Ability to work independently on technical tasks while collaborating effectively with a multidisciplinary team.

• Genuine interest in music, creative tools, and how technology can expand musical expression.

• Academic projects, research experience, or personal projects involving audio processing, music generation, or machine learning.

• Familiarity with generative audio models, including diffusion-based, autoregressive, or other neural audio architectures.

• Experience with audio libraries, such as librosa or torchaudio, or tools for audio analysis and manipulation.

• Familiarity with audio embeddings, neural audio codecs, text-audio models, or multimodal learning.

• Experience with music production software, digital audio workstations (DAWs), audio plugins, or creative coding tools.

• Experience collaborating with musicians, producers, or other creative professionals.

• Familiarity with evaluating generative models or assessing subjective qualities such as musicality, expressiveness, and controllability.

• Interest in responsible AI practices, including artist consent, music rights, and ethical dataset development.

• Contributions to research projects, open-source software, machine learning competitions, or relevant technical communities.

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, developing tools that support creative expression while recognizing 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 interns who combine technical curiosity with a thoughtful approach to building AI responsibly and who are excited to help develop technology that benefits both music creators and listeners.

• Hands-on experience researching and building AI-powered music tools.

• The opportunity to work on real-world challenges in generative AI, audio modeling, and music technology.

• Close collaboration with researchers, engineers, music technologists, and producers.

• The opportunity to experiment with emerging technologies and contribute ideas to product development.

• Exposure to the process of turning research prototypes into practical creative tools.

• Mentorship and support for continued technical learning and professional growth.

• A chance to help shape the future of music discovery, creation, and expression in a responsible, creative environment.