Senior Machine Learning Engineer, Developer Product Analytics

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. Product Data Science sits within Apple Services Engineering, the org that runs Apples content platforms end-to-end. The team builds the intelligence layer behind partner-facing analytics applications and Apples 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 Apples platforms have to offer.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.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.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.

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.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.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.

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. Product Data Science sits within Apple Services Engineering, the org that runs Apples content platforms end-to-end. The team builds the intelligence layer behind partner-facing analytics applications and Apples global content charts. Recent