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Apple · Beijing, China

# AIML - ML Platform Engineer (Apple Ecosystem), Data Intelligence Platform

Beijing, China Data & Analytics 

Listed on Apple’s official careers site. Posted April 9, 2026. Check that it is still open and apply there.
[Apply on Apple’s careers site →](https://jobs.apple.com/en-us/details/200656176/aiml-ml-platform-engineer-apple-ecosystem-data-intelligence-platform)

[OpenNash Jobs](https://jobs.opennash.com/) / [Apple](https://jobs.opennash.com/companies/apple/) / AIML - ML Platform Engineer (Apple Ecosystem), Data Intelligence Platform

## Job description

We are looking for engineers with a passion for using machine learning to create intelligent applications. Be part of a highly accomplished, deeply technical and close-knit team of machine learning engineers, you will create tools that are used by millions of people. You will design and implement new machine learning algorithms and techniques, and collaborate with the most innovative product development teams in the world.

This team builds the infrastructure that enables product teams across Apple to develop machine-learning solutions that power amazingly intelligent user experiences. We are looking for new energetic members to join the ML team. In this role, you will build the infrastructure, tools, and develop machine-learning solutions. You will also have the opportunity to engage with exciting new-product teams around Apple, and use your machine learning skills to solve challenging technical problems in our next-generation products that will delight millions of people.

## At a glance
- Posted by the employer: Apr 9, 2026
- First seen by OpenNash: Jul 10, 2026
- Last checked: Oct 18, 2026
- Listed for: 192 days
- Location: Beijing, China[Apply on Apple’s careers site →](https://jobs.apple.com/en-us/details/200656176/aiml-ml-platform-engineer-apple-ecosystem-data-intelligence-platform)

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OpenNash lists jobs from employers’ own careers sites and does not handle applications.
Dates come from the employer where stated; “first seen” and “last checked” are our own observations.

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