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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and developing machine learning models for music generation and transformation, with a strong foundation in audio signal processing and generative audio techniques. Proficient in Python and modern ML frameworks, capable of translating research into practical applications while collaborating effectively with creative professionals.
Highest-signal resume keywords
Machine LearningDeep LearningAudio Signal ProcessingPython ProgrammingGenerative Audio
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning ModelsDeep Learning TechniquesStatistical MethodsAudio Representation LearningSource SeparationSignal ProcessingEvaluation FrameworksNeural Audio ArchitecturesAudio CodecsText-Audio Models
Soft Skills
CollaborationCommunicationCreative Workflow Understanding
Tools & Technologies
PyTorchAudio Processing LibrariesDAWsAudio PluginsWorkflow Orchestration Tools
Certifications & Qualifications
PhD or MS in Computer ScienceMachine LearningAudio Signal ProcessingMusic Technology
Industry Keywords
Generative AIResponsible AI PracticesMusic RightsArtist ConsentData Provenance
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- 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
What you’ll need- 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
Comp & perks- 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.