Research Scientist, AI Music

Job not on LinkedIn

🔥 11 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧠 AI Research Scientist

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Hook

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11 - 50 employees

📱 Media

👥 B2C

💰 $3M Seed Round - Hook on 2025-03

Media • B2C

Hook is a mobile-first social music discovery app from Hook Music Inc. It lets users browse and remix short music “hooks,” participate in challenges and leaderboards, and engage with creator-driven audio content and filters. The platform is consumer-focused, emphasizing discovery, social sharing, and user-generated remixes and collaborations.

📋 Description

• Design and develop machine learning models for music generation, transformation, remixing, and creative interaction. • Investigate advances in generative audio, music understanding, audio representation learning, and related machine learning areas. • Translate research ideas and prototypes into practical, high-quality AI capabilities with engineering and music technology teams. • Design experiments and evaluation frameworks assessing audio quality, musicality, controllability, performance, and creative usefulness. • Collaborate with producers, musicians, and cross-functional teams to understand creative workflows and develop intuitive, musically meaningful tools. • Consider music rights, training data provenance, artist consent, and attribution throughout model development.

🎯 Requirements

• 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. • 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.

🏖️ Benefits

• 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.

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