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Apple

# Sr Machine Learning Engineer - ML Platform

New York City, United States of America Senior individual contributor Data & Analytics 

Listed on Apple’s official careers site. Posted September 16, 2026. Check that it is still open and apply there.
[Apply on Apple’s careers site →](https://jobs.apple.com/en-us/details/200683782/sr-machine-learning-engineer-ml-platform)

[OpenNash Jobs](https://jobs.opennash.com/) / [Apple](https://jobs.opennash.com/companies/apple/) / Sr Machine Learning Engineer - ML Platform

## Job description

At Apple, we work every day to create products that enrich people's lives. Our Apple Ads group makes it possible for people around the world to easily access informative and imaginative content on their devices while helping publishers and developers promote and monetize their work. Today, our technology and services power advertising in the App Store, Apple Maps, and Apple News. Our platforms are highly-performant, deployed at scale, and setting new standards for enabling effective advertising while protecting user privacy.

The Apple Ads Machine Learning Platform team's mission is to empower Ads teams to build and scale the innovative ML systems that deliver highly optimized advertising content to consumers. Are you a results-oriented and versatile engineer who can excel in a fast-paced environment? You will work closely with ML engineers and scientists to design, develop, and build world-class platform capabilities that will enable Ads teams to improve and scale our ML features, models, and applications.

## At a glance
- Posted by the employer: Sep 16, 2026
- First seen by OpenNash: Oct 11, 2026
- Last checked: Oct 18, 2026
- Listed for: 32 days
- Location: New York City, United States of America[Apply on Apple’s careers site →](https://jobs.apple.com/en-us/details/200683782/sr-machine-learning-engineer-ml-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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