ML Engineer (Applied AI)

TL;DR

ML Engineer (Applied AI): Building and scaling high-performance AI models for music and market data with an accent on productionizing research prototypes. Focus on implementing MLOps best practices, leveraging Generative AI and LLMs, and driving predictive intelligence across the business intelligence ecosystem.

Location: Must be based in London, UK (Hybrid role)

Company

A global music and entertainment company home to premier recording labels and publishing catalogs, championing both emerging artists and global superstars.

What you will do

• Design, develop, and benchmark state-of-the-art machine learning models for forecasting, segmentation, and recommendation.
• Lead technical exploration into Generative AI, LLMs, and RAG architectures to create automated analysis products.
• Translate validated research prototypes into robust, production-ready systems.
• Implement MLOps best practices including CI/CD, monitoring, and model serving.
• Perform complex feature engineering on multi-modal data to support experimental models.
• Collaborate with product managers and business stakeholders to ensure technical solutions deliver tangible impact.

Requirements

• Bachelor’s degree in Applied Mathematics, Computer Science, or a related quantitative discipline (Master’s preferred).
• 2+ years of professional experience as a Machine Learning Engineer or Applied ML Scientist.
• Strong expertise in Python development and the full lifecycle management of complex ML/DL models.
• Proven track record of transforming R&D proofs-of-concept into scalable production-grade solutions.
• Solid background in software engineering best practices including clean code, testing, and Git.
• Exceptional communication skills for explaining complex model limitations to non-technical stakeholders.

Culture & Benefits

• Work within a global, innovative music company that retains a nimble, creative spirit.
• Commitment to an inclusive and diverse work environment.
• Opportunity to work at the intersection of cutting-edge AI and the global music industry.
• Focus on collaboration, curiosity, and integrity across all business units.