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.

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.

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.

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.