Location
London

Hours
Full Time

Salary
Negotiable

About the Role
Join the team that helps all Apple Music users discover music they will love. Our team is behind some of the most popular features in Apple Music, including the Home and New tabs, Discovery Station, and Playlist Playground. Music is our passion, and our aim is to connect artists with music fans. We focus on two key people: the listener trying to find their next favourite, and the artist trying to be found. Our diverse team, representing 10 countries, fosters an open-minded environment where we help each other do amazing work and grow.

At Apple, innovation never stops. Bring dedication to your job, and you will be part of the innovation that enriches our users' lives. The possibilities are boundless. Your work at Apple Music will become part of a product that deeply cares for music and for the privacy of our users in a way no other company can match. We operate at massive scale across a wide variety of personalization products that touch every aspect of the Apple Music experience.

You will research AI/ML models for recommendation, both bespoke and foundational, that push the state of the art. You will train and fine-tune these models on huge GPU grids and massive quantities of data, and help deploy them into our large-scale, low-latency services. You will run experiments, translate results into product decisions, and publish your findings. You will work alongside some of the best researchers and engineers in the field, connected to Apple’s wider internal ML research community. We hire great people and trust them to do their best work. It’s the people who make it exciting to work here every day, and you will be one of them.

If this sounds like you, we’d love to hear from you.

Experience
- Track record of leading ML recommender system projects from research through to production at scale
- Peer-reviewed publications at venues such as RecSys, SIGIR, KDD, ISMIR, NeurIPS, ICLR, ICML or related
- Expertise in modern recommender methods (e.g. multi-interest, neural ranking, reinforcement learning, sequential, generative)
- Solid experience with Python ML toolkits such as TensorFlow or PyTorch
- Familiarity with large language model (LLM) methods applied to recommendation
- Experience with counterfactual evaluation
- Experience with Spark SQL

About you
- Excellent communication and presentation skills
- Passionate about music and technology

Qualifications
A PhD or MSc in computer science, statistics, applied mathematics or related field, or equivalent education and experience