Senior Machine Learning Engineer - Ads Auction

In this role, you are responsible for designing the core auction system that powers our advertising platform to drive optimal outcomes for advertisers, users, and our platform. You will have the opportunity to build the next generation solutions for marketplace optimization that enable driving optimal value for multiple stakeholders in the system. You will have the opportunity to apply your ability to move the state of the art techniques in a fast growing business that positively impacts publishers, developers and Apple users at global scale. The ability to be a great teammate under tight deadline constraints is key to success.

You will be responsible for designing, analyzing, and optimizing the core auction system that powers our advertising platform, develop production code to generate high quality ad recommendations and work closely with business partners to help drive the development of new products as well as perform large scale and complex experiments to understand their effects.
You will drive strategic outcomes through substantial innovation in multiple fields by leading the development and application of advanced techniques and algorithms to improve our ad network.
You have, or will develop a deep understanding of the ad network behavior, and will work with product management and business leadership to prioritize an innovation roadmap across multiple technical domains.
You will lead the conception, development, and delivery of state of the art capabilities that differentiate our products and are core to our business.
You will work with a variety of cross functional business partners to set strategy and bring end-to-end solutions that scale as we grow.

5+ years of experience working in online advertising, marketplace design, or large-scale recommendation/auction systems
Strong background in auction theory, mechanism design, optimization, statistics, or machine learning.
Ability to apply and implement research concepts, ultimately in production quality code
Experience defining clear, testable research hypotheses, including intended impact on the business
Deep knowledge of design of experiments, online experimentation approaches, preferably at scale
Ability to formulate and advocate for R&D objectives and results to cross-functional team members including executive business leadership and product management
Experience contributing and/or reviewing research for top conferences and publications
Deep fluency in Java or Python.
Experience with Spark, Hadoop or other distributed frameworks.
PhD in Economics, Operations Research, Machine Learning, Statistics, Control Theory, Forecasting, Optimization, Reinforcement Learning or related field with experience building production systems or have equivalent experience working with large data science / machine learning projects in industry.

Experience in ads optimization, recommendations, or search relevance optimization is highly preferred

At Apple, we focus deeply on our customers’ 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, and MLS Season Pass.

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 big, global brands. Because when advertising is done right, it benefits everyone.

We are seeking a self-motivated individual that will build out the next generation of our ads platforms and ensure that Apple provides the most relevant and high quality ads experience while maintaining a healthy marketplace.

The ideal candidate has a strong background in auction theory, applied machine learning, and large-scale systems, along with hands-on experience building and optimizing auction-based ad delivery systems in production. You will have an excellent understanding of scalable architectures and thrive working in Agile environments.

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 $181,100 and $318,400, 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

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

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

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Apple accepts applications to this posting on an ongoing basis.