We are looking for a highly skilled and experienced Machine Learning Engineer to join our team and drive innovation in music and audio technology.
The ideal candidate will have a strong background in machine learning, audio signal processing, and music theory. You will conduct research on novel machine learning techniques for music and audio applications, develop and deploy these models into real-world products and services, and collaborate closely with engineers and scientists to solve complex challenges.
Your work will directly impact how users experience and interact with music technology.
Key Responsibilities
- Develop and apply state-of-the-art machine learning and deep learning models to tackle challenges in music generation, source separation, audio enhancement, and music information retrieval
- Conduct research on innovative machine learning techniques tailored for music and audio applications
- Deploy machine learning solutions in production environments powering real-world products and services
- Collaborate closely with multidisciplinary teams of engineers, researchers, and product managers to solve complex problems in music and audio technology
Skills, Knowledge and Expertise
- Master's or PhD degree in Computer Science, Machine Learning, or a related field (equivalent work experience will also be considered)
- Strong proficiency in Python programming, with hands-on experience using ML frameworks such as PyTorch and TensorFlow, and audio processing libraries like Librosa
- Solid understanding of state-of-the-art ML models and techniques
- Good understanding of basic music theories such as chords, scales, and intervals
- Familiarity with MIDI and Audio formats, including MIDI message structures and audio file formats
- Strong knowledge in digital signal processing
- Excellent problem-solving and analytical skills, with attention to detail
- Ability to work both independently and collaboratively in a fast-paced environment
- Effective communication skills and the ability to translate technical concepts to non-technical stakeholders
Preferred qualifications
- Background or strong interest in music, with knowledge of music theory and composition
- Experience in one or more areas of music AI, such as:
- Music Generation
- Source Separation
- Audio Enhancement
- Music Transcription (chords, notes)
- Singing Voice Synthesis
- Music Information Retrieval
- A strong record of first-author publications in leading AI conferences or journals (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, ICASSP)
- Proficiency in C/C++ and shell programming languages, with a deep understanding of data structures and algorithm design
- Experience with MLOps frameworks, such as MLFlow or KubeFlow
