Machine Learning Researcher - Apple Music - Recommender Systems
Join the team that helps all Apple Music users discover music they will love. We are 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. Two people are always on our minds: the listener trying to find their next favourite, and the artist trying to be found.
Our team members come from 10 countries, creating a diverse, open\-minded environment in which we help each other do amazing work and grow.
Here 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.
Description
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 work at massive scale and across a wide variety of personalisation products that touch every aspect of the Apple Music experience.
You will research AI/ML models for recommendation, bespoke and foundational, that push the state of the art. You will train and fine\-tune them 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 what you find.
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.
Is this you? If so, we'd love to hear from you.
Preferred Qualifications
Familiarity with LLM methods applied to recommendation
Experience with counterfactual evaluation
Experience with Spark SQL
Love of music
Minimum Qualifications
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, RL, sequential, generative)
Solid experience with Python ML toolkits such as TensorFlow or PyTorch
Excellent communication and presentation skills
A PhD/MSc in computer science, statistics, applied mathematics or related field, or equivalent education/experience
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