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# Machine Learning Engineer, Developer Product Analytics

- **Company:** Apple Inc.
- **Location:** Cupertino, CA, United States
- **Experience:** Expert
- **Salary:** $184,700.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Algorithm Design, Data Analysis, Distributed Data Store, Python (Programming Language), Machine Learning, Service Design, Software Deployment, Software Engineering, Usage Analysis, Large Language Models, Multi-Agent Systems, Apache Spark, Deep Learning, Information Technology, Unsupervised Learning
- **Published:** September 1, 2026
- **Apply:** https://www.themuse.com/jobs/apple/senior-machine-learning-engineer-developer-product-analytics-1f9e3d

## About the Role

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., 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.

## Description

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.


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