Apple Inc.
Apple Music - Machine Learning Engineer (Personalized Stations and Mixes)
Apple Inc., Cupertino, California, United States, 95014
Apple Music - Machine Learning Engineer (Personalized Stations and Mixes) 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.
Apply (by clicking the relevant button) after checking through all the related job information below. We’re also part of a larger team at Apple Services Engineering and beyond. We work together to realize 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 Cupertino-based 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. Improving the quality of our personalized music streaming experiences. Working with ML researchers 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. Proven track record of collaborating with ML researchers to develop and improve highly scaled, ML-backed products. Demonstrated understanding of the full lifecycle of online ML-based solutions and related best practices. Strong programming skills in at least one modern object-oriented language such as Java, Python, C++. Ability to excel in a multi-functional environment through clear communication and relationship building. Preferred Qualifications Experience with recommender systems or personalization and an understanding of the related algorithms and models. Experience building and maintaining online ML systems with real-time feedback (e.g. online bandit models). Experience with building and maintaining ML and big data pipelines. Experience with Kubernetes and modern deployment patterns. At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $175,800 and $312,200, and your base pay will depend on your skills, qualifications, experience, and location. Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program. Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
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Apply (by clicking the relevant button) after checking through all the related job information below. We’re also part of a larger team at Apple Services Engineering and beyond. We work together to realize 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 Cupertino-based 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. Improving the quality of our personalized music streaming experiences. Working with ML researchers 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. Proven track record of collaborating with ML researchers to develop and improve highly scaled, ML-backed products. Demonstrated understanding of the full lifecycle of online ML-based solutions and related best practices. Strong programming skills in at least one modern object-oriented language such as Java, Python, C++. Ability to excel in a multi-functional environment through clear communication and relationship building. Preferred Qualifications Experience with recommender systems or personalization and an understanding of the related algorithms and models. Experience building and maintaining online ML systems with real-time feedback (e.g. online bandit models). Experience with building and maintaining ML and big data pipelines. Experience with Kubernetes and modern deployment patterns. At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $175,800 and $312,200, and your base pay will depend on your skills, qualifications, experience, and location. Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program. Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
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