Senior Machine Learning Engineer - Recommendations (Core)

Location: Berlin or London, United Kingdom

Company

hirify.global is a music platform connecting artists with listeners through recommendations, search, and content understanding.

What you will do

• Design, train, and ship models for candidate generation, user and item modeling, ranking, search, and content understanding.
• Lead the migration from classical approaches to PyTorch-based deep-learning models trained and served on GPUs.
• Adapt, fine-tune, and evaluate open-source embedding and ranking models for the catalog and use cases.
• Own offline metrics, A/B experiments, and the connection between offline improvements and online performance.
• Collaborate with ML, data, and backend engineers to productionize and maintain models.
• Help establish modeling standards across the ML Core team.

Requirements

• 3+ years building ML systems in production, including end-to-end ownership of at least one ranking, retrieval, or recommendation model.
• Strong deep-learning skills in PyTorch or similar frameworks and a solid understanding of embeddings, two-tower models, learning to rank, multi-objective ranking, and their trade-offs.
• Experience evaluating and experimenting with ranking systems, including biases in implicit feedback such as clicks and plays.
• Strong Python and experience with large-scale data processing such as Spark or BigQuery.
• Experience deploying models to production on a cloud platform; GCP is preferred.
• M.Sc. in a quantitative field or equivalent experience.

Nice to have

• Experience fine-tuning or serving transformer-based embedding and ranking models.
• Experience applying LLMs or foundation models to recommendations, including content understanding, semantic IDs, generative retrieval, or feature generation.
• Audio or music ML experience and multimodal representations.
• Experience with contextual bandits, reinforcement learning, or causal methods for long-term value.
• Publications at RecSys, KDD, SIGIR, WSDM, NeurIPS, or similar conferences.
• Experience with ANN or vector search and GPU training or serving at scale.

Culture & Benefits

• Work within the ML Core team on shared models supporting recommendations, search, and content understanding.
• Collaborate across ML, data, and backend engineering.
• Applicants are encouraged to apply even if they do not meet every qualification.