Senior machine learning engineer - music recommendation engine

Senior Machine Learning Engineer - Music Recommendation Engine

London, England, United Kingdom Machine Learning and AI

Here at Apple new ideas have a way of becoming great products very quickly, and innovation never stops. Bring passion and dedication to your job and there's no telling what you could accomplish. The Music ML team within Apple Services Engineering is responsible for personalisation and recommendation in Apple Music. We are looking for an experienced Software or Machine Learning Engineer to help design and run our customer-facing recommendation services reliably, efficiently, and with dedication to delivering relevant and diverse music to our users. Music is our passion, and our aim is to connect artists to music lovers like ourselves. We build amazing experiences for our users while respecting their privacy. Our team is a friendly bunch of people from more than 10 countries. We help each other grow and realise the best work for our users. We’re also part of a larger team at Apple Services Engineering and beyond. We work together to realise a single unified vision, making use of Apple’s unique integration of hardware, software, and services. And although services are a bigger part of Apple’s business than ever before, these teams remain small, nimble, and cross-functional, offering great opportunities to collaborate and grow.

Description

The Music ML team within Apple Services Engineering is looking for a great Software or Machine Learning Engineer to build and improve the features and services driving Apple Music personalisation. Our team is responsible for providing personalised features for Apple Music including Home, New, Radio, and Personal Mixes. Our work includes data analysis, large-scale offline pipelines, machine‑learned model training and inference, and online services to provide real‑time personalised experiences. Our growing London‑based team builds and evolves global‑scale, leading‑edge machine learning systems and distributed systems that serve recommendations to users around the world. We are responsible for the full lifecycle: collaboration with the Product team, system design, implementation, continuous optimisation and improvement.

Responsibilities

• Building products and services for millions of users with a focus on great customer experience and privacy
• Developing complex systems that integrate data from many sources to deliver on‑the‑fly personalisation with low latency
• Tuning performance considering both latency and throughput
• Deploying our systems globally for improved resiliency and end‑user experience
• Collaborating across teams to take new user‑facing features from conception to production
• Working within our team to develop and deploy massive datasets to improve personalised features
• Prototyping algorithm changes and launching A/B tests to measure changes to personalised products

Minimum Qualifications

• Hands‑on experience engineering and maintaining large distributed backend systems
• Hands‑on experience with applied machine learning systems at production scale
• Understanding of concurrency, algorithms and object‑oriented programming
• Effective collaboration with researchers to improve machine learning models

Preferred Qualifications

• A vision of how to engineer modern ML‑driven pipelines, APIs and services at scale, with fast iteration cycles
• Ability to own and lead projects from conception through to design and implementation
• Experience with recommender systems, feature engineering
• Experience designing and running customer‑facing A/B tests

At Apple, we're not all the same. And that's our greatest strength. We draw on the differences in who we are, what we've experienced and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. As a registered Disability Confident employer, we will work with applicants to make any reasonable accommodations. Apple will consider for employment all qualified applicants with criminal backgrounds in a manner consistent with applicable law. Learn more

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