Design and implement ML algorithms to enhance music creation tools and solve various user problems in line with product goals
Identify and implement off-the-shelf ML and AI tools to solve practical problems efficiently
Understand the requirements of running models in production, including domain shift testing, QA, A/B testing and so on
Maintain production-ready code with considerations for how solutions fit the product and enhance the user experience
Build scalable, maintainable data pipelines to handle audio and other unstructured data
Collaborate with Product and Engineering teams to ensure seamless integration of ML solutions into production systems
Evaluate, deploy, and fine-tune pre-trained models for tasks like audio analysis, melody generation, and process automation
Uphold ethical AI practices, ensuring fairness and responsible AI use in music-related applications

Proven software development experience, ideally in Python (other languages a plus)
Experience implementing and deploying ML models, using PyTorch framework
Familiarity with AWS cloud environment for deploying and scaling ML solutions
Ability to preprocess and model unstructured data, especially audio
A strong focus on applied problem-solving, with a practical approach to integrating existing tools and systems
A good understanding of music, production, or audio technology processes (or a strong interest in music)
Familiarity with GenAI architectures like transformers, LLMs, or diffusion models
Clear and effective communication with technical and non-technical stakeholders
Ability to work independently and remotely while collaborating closely with cross-functional teams

Flexible work arrangements
Opportunity to contribute to cutting-edge music technology
Work directly with industry veterans from Spotify, Soundcloud, Twitch, and YouTube
Be part of a passionate, innovative team redefining music creation

Applied ML Engineer

Key skills