Job Details

Role Number: 200628841-0836

Summary

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.

Description

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.

Minimum Qualifications

• 4+ years of experience building machine learning capabilities across many different product areas at scale

• Strong proficiency in Java, Python, or Scala for algorithm and system development.

• Experience with distributed systems and big data frameworks such as Spark, Kafka, Hadoop, or Flink.

• Solid understanding of data structures, algorithms, and system design principles.

• Familiarity with CI/CD workflows, cloud environments, and containerized deployments.

• Knowledge of data validation, cleansing, and quality assurance practices.

• Understanding of statistical methods, A/B testing, and online experimentation frameworks.

• BS or MS in Computer Science, Software Engineering or related technical fields.

Preferred Qualifications

• 7+ years of experience building machine learning capabilities across many different product areas at scale.

• Background in Advertising systems.

• Contributions to open-source algorithm frameworks or data processing tools.

• Prior experience working with Anomaly detection is a plus.

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. (https://www.apple.com/careers/us/benefits.html)

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) .