Software Engineer - Apple Music
Job Description
Summary
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 at Apple Services Engineering is responsible for personalization and recommendations in Apple Music. We are looking for an experienced Software Engineer to help design and run our customer-facing recommender services reliably, efficiently, and with dedication to delivering to our users the music they will love to listen to.
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 realize 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.
The Music ML team at Apple Services Engineering is responsible for personalization and recommendations in Apple Music. We are looking for an experienced Software Engineer to help design and run our customer-facing recommender services reliably, efficiently, and with dedication to delivering to our users the music they will love to listen to.
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 realize 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 at Apple Services Engineering is looking for a great Software Engineer to build and improve the features and services driving Apple Music personalization.
Our team is responsible for providing personalized features for Apple Music including some of the most high-traffic surfaces such as Home, Radio, and Personal Mixes. Our work includes building and maintaining ML-backed high-throughput online services, online experimentation and data analysis, as well as building and maintaining large-scale offline big data pipelines. Here you have a phenomenal opportunity to help build and evolve global-scale, leading-edge dynamic data systems as we grow our amazing team.
We are responsible for the full lifecycle: collaboration with the Product team, system design, implementation, continuous optimization and improvement.
WHAT YOU WILL BE WORKING ON:
- 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 personalization at low latencies
- 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 personalized features
- Prototyping algorithm changes and launching A/B tests to measure changes to personalized products
If this sounds exciting to you, we’d love to hear from you!
Our team is responsible for providing personalized features for Apple Music including some of the most high-traffic surfaces such as Home, Radio, and Personal Mixes. Our work includes building and maintaining ML-backed high-throughput online services, online experimentation and data analysis, as well as building and maintaining large-scale offline big data pipelines. Here you have a phenomenal opportunity to help build and evolve global-scale, leading-edge dynamic data systems as we grow our amazing team.
We are responsible for the full lifecycle: collaboration with the Product team, system design, implementation, continuous optimization and improvement.
WHAT YOU WILL BE WORKING ON:
- 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 personalization at low latencies
- 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 personalized features
- Prototyping algorithm changes and launching A/B tests to measure changes to personalized products
If this sounds exciting to you, we’d love to hear from you!
Minimum Qualifications
- BS or MS degree in a quantitative field, such as Computer Science, Applied Mathematics, or Statistics
- 5+ years of industry experience designing, building, maintaining, and extending web-scale distributed systems
- Proven track record of collaborating with ML researchers to develop and improve highly-scalable, low latency, ML-backed services
- Strong programming skills including an understanding of concurrency and algorithms in Java
- Demonstrated commitment to driving operational excellence and software maintainability best practices within software engineering teams
- Ability to excel in a multi-functional environment through clear communication and relationship building
Preferred Qualifications
- Experience building and maintaining online ML systems with real-time feedback (e.g. online bandit models)
- Experience with recommender systems or personalization and an understanding of the related algorithms and models
- Experience with building and maintaining ML and big data pipelines
- Experience with Kubernetes and modern deployment patterns
