Senior Machine Learning Engineer, Developer Product Analytics
19 days ago Cupertino, CA
About us
Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.
- Size: 10,000+ employees
- Industry: Technology: Manufacturing
Why you should apply for a job to Apple:
Ranked as one of the Best Technology Companies in 2020
3.7/5 in overall job satisfaction
3.1/5 in supportive management
66% say women are treated fairly and equally to men
65% would recommend this company to other women
66% say the CEO supports gender diversity
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Apple Services Engineering powers the digital storefronts and partner platforms that millions rely on every day, from the App Store, Apple Music, and Podcasts to the analytics platforms that serve the developers and artists who create for them (App Store Analytics, Apple Music for Artists, Podcast Analytics). The Product Data Science team builds the statistical, ML, and AI-powered algorithms behind these platforms, focused on content-partner analytics tools, experimentation engines, privacy-preserving analytics, and charting systems used by millions of businesses and users worldwide. We are looking for a scientist who has shipped end-to-end ML solutions in production, is driven to find the next high-impact problem, and wants to do it at Apple scale.
Description
Product Data Science sits within Apple Services Engineering, the org that runs Apple's content platforms end-to-end. The team builds the intelligence layer behind partner-facing analytics applications and Apple's global content charts. Recent examples of our work include a Bayesian experimentation engine that powers Product Page Optimization in App Store Analytics, and differential privacy solutions behind the Peer-Group Benchmarks feature, giving developers privacy-safe performance insights they could not get anywhere else. We stay close to the research and encourage the team to do the same, whether in Bayesian methods, privacy-preserving ML, or applied AI. There are regular opportunities to present work at internal tech talks and external conferences. We care deeply about translating research into features that give content partners materially useful insights, and help users discover more of what Apple's platforms have to offer.","responsibilities":"Work with product managers, cross-functional engineering teams, and business partners across time zones to identify high-impact opportunities.
Own the full scientific product lifecycle: problem framing, data exploration, algorithm design, model training, and production deployment.
Take 0-to-1 features end-to-end, from problem framing through production deployment.
Ship your work as features used by content partners, businesses, and users globally.
Build conviction with senior product and engineering stakeholders and drive technical direction forward.
Translate research into features that deliver materially useful insights to content partners and users.
Preferred Qualifications
3-5+ years of industry experience designing and deploying ML or statistical solutions in production.
Experience with differential privacy, causal inference, or statistical experimentation (A/B testing, Bayesian experimentation).
Familiarity with distributed data platforms and web-scale pipelines.
Exposure to applied AI, LLMs, and agentic systems.
Production engineering experience in Scala or Spark.
You think in user outcomes, not model metrics.
Communicates clearly across technical and non-technical audiences, and across time zones.
Comfortable working independently and collaboratively in a geographically distributed, cross-functional org.
Minimum Qualifications
First-principles understanding of the methods you use: able to explain why an algorithm works, its assumptions, and where it breaks.
Proficiency across multiple ML domains: supervised and unsupervised learning, deep learning, time-series modeling, and Bayesian statistics.
Production-quality software engineering in Python, including reusable service design and the full deployment lifecycle.
Experience taking 0-to-1 features end-to-end: problem framing, algorithm design, and production deployment.
MS or PhD in Statistics, Computer Science, Machine Learning, or a related quantitative field. Candidates with equivalent industry experience will be considered.
Pay & Benefits
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 $184,700 and $277,600, 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.
Description
Product Data Science sits within Apple Services Engineering, the org that runs Apple's content platforms end-to-end. The team builds the intelligence layer behind partner-facing analytics applications and Apple's global content charts. Recent examples of our work include a Bayesian experimentation engine that powers Product Page Optimization in App Store Analytics, and differential privacy solutions behind the Peer-Group Benchmarks feature, giving developers privacy-safe performance insights they could not get anywhere else. We stay close to the research and encourage the team to do the same, whether in Bayesian methods, privacy-preserving ML, or applied AI. There are regular opportunities to present work at internal tech talks and external conferences. We care deeply about translating research into features that give content partners materially useful insights, and help users discover more of what Apple's platforms have to offer.","responsibilities":"Work with product managers, cross-functional engineering teams, and business partners across time zones to identify high-impact opportunities.
Own the full scientific product lifecycle: problem framing, data exploration, algorithm design, model training, and production deployment.
Take 0-to-1 features end-to-end, from problem framing through production deployment.
Ship your work as features used by content partners, businesses, and users globally.
Build conviction with senior product and engineering stakeholders and drive technical direction forward.
Translate research into features that deliver materially useful insights to content partners and users.
Preferred Qualifications
3-5+ years of industry experience designing and deploying ML or statistical solutions in production.
Experience with differential privacy, causal inference, or statistical experimentation (A/B testing, Bayesian experimentation).
Familiarity with distributed data platforms and web-scale pipelines.
Exposure to applied AI, LLMs, and agentic systems.
Production engineering experience in Scala or Spark.
You think in user outcomes, not model metrics.
Communicates clearly across technical and non-technical audiences, and across time zones.
Comfortable working independently and collaboratively in a geographically distributed, cross-functional org.
Minimum Qualifications
First-principles understanding of the methods you use: able to explain why an algorithm works, its assumptions, and where it breaks.
Proficiency across multiple ML domains: supervised and unsupervised learning, deep learning, time-series modeling, and Bayesian statistics.
Production-quality software engineering in Python, including reusable service design and the full deployment lifecycle.
Experience taking 0-to-1 features end-to-end: problem framing, algorithm design, and production deployment.
MS or PhD in Statistics, Computer Science, Machine Learning, or a related quantitative field. Candidates with equivalent industry experience will be considered.
Pay & Benefits
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 $184,700 and $277,600, 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.
Additional Information
- Salary: $184,700 – $277,600
- Posted: 19 days ago