Staff Machine Learning Engineer – Ads Platform
Apple - Austin, United States of America
Posted Feb 19, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
- Not verified last checked Jun 13, 2026
- Salary
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- 401(k) match
- Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Staff Machine Learning Engineer – Ads Platform Austin, United States of America At Apple, we focus deeply on the customer experience. Apple Ads brings this same approach to advertising, helping people find exactly what they're looking for and helping advertisers grow their businesses. Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, MLS Season Pass and now F1 ! . Everything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes-from small app developers to news publishers to big, global brands. Because when advertising is done right, it benefits everyone. Apple Ads is Hiring a hands-on Machine Learning Engineer. In this role you will build design and build Machine learning systems and data pipelines to safeguard the advertiser trust of our platform and enhance invalid traffic protections. You will define and execute an innovation roadmap; build and deploy models with robust CI/CD, feature stores, and streaming infrastructure (e.g., Kafka/Spark/Flink); and run A/B experimentation. You will lead performance tuning, calibration, and drift detection to deliver measurable improvements in product quality, user experience, latency, and cost. Develop and manage end-to-end lifecycle of machine learning models, including observability for large-scale, high-throughput, and low-latency production systems. Design, develop, and optimize distributed algorithms and data processing frameworks(e.g., Spark). Implement scalable feature pipelines to ingest, clean, transform, and analyze massive datasets. Reinforce Ads integrity and advertiser trust by safeguarding infrastructure. Solve complex problems
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