Staff Machine Learning Engineer - Matching, Apple Ads

Apple, Inc.

Cupertino (CA)

On-site

USD 216,000 - 325,000

Full time

4 days ago
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Benefits offered by this job

Employee stock programs
Discretionary bonuses or commissions
Relocation assistance
Medical and dental coverage

Job summary

Apple Ads is seeking a Staff Machine Learning Engineer - Matching in Cupertino to build state-of-the-art retrieval algorithms for displaying relevant ads. You will own end-to-end development from ideation to production, collaborating with product and business teams while prioritizing privacy-friendly design.

You will apply NLP, information retrieval and ML at scale, contribute to research, and help differentiate Apple's ad products in an Agile environment.

Qualifications

  • 7+ years of experience building ML capabilities at scale.
  • Proven experience in NLP, information retrieval and search.
  • Ability to apply and implement research concepts in production quality code.
  • Experience defining clear, testable research hypotheses with business impact.
  • Deep understanding of design of experiments and online experimentation at scale.
  • Experience contributing and/or reviewing research for top conferences and publications.
  • Master's, or equivalent in NLP, ML, Statistics, Forecasting, Optimization, RL or related field with production systems experience.

Responsibilities

  • Develop algorithms for our retrieval system.
  • Drive step-change improvements in outcomes and platform revenue.
  • Own ideation, development, testing and productization end-to-end.
  • Work with cross-functional teams to ensure feasibility and robustness.
  • Present findings across the organization.

Skills

Machine learning
NLP
Information retrieval
Production code
Design of experiments
Research publications

Education

Master's in NLP/ML/Statistics/Forecasting/Optimization/RL
Equivalent industry experience

Job description

Staff Machine Learning Engineer - Matching, Apple Ads

Cupertino, California, United States Software and Services

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!You'll have the opportunity to develop models that improve our platform across the board, write production code to generate recommendations, 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'll 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've, 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'll lead the conception, development, and delivery of state of the art capabilities that differentiate our products and are core to our business. You should have experience developing and implementing machine learning algorithms, ideally within the ads space. You'll have an excellent understanding of scalable architectures and thrive working in Agile environments. The ability to be a good team player under tight deadline constraints is key to success.

Description

Matching at Ads is a critical component of our Ads funnel and responsible for ensuring that we are retrieving relevant and engaging Ads for our users. We are building the next generation of our retrieval systems to sustain the growth of the business for the future. We are looking for a Machine Learning Engineer that will develop the algorithms for our retrieval system.In this role you will drive step-change improvements in our outcomes, impact our platform revenue and quality of our ads, and formulate approaches to greenfield product opportunities. You will own end-to-end the ideation, development, testing and productization of these algorithms. You will work with complex problems in the ads retrieval space, provide solution that adhere to Apple’s privacy principles, review and contribute to state of the art in research, work with a variety of cross functional teams to ensure the feasibility and robustness of delivering new capabilities. This role will work closely with organizational partners and be expected to present findings across the organization.

Minimum Qualifications
  • 7+ years of experience building machine learning capabilities across many different product areas at scale.
  • Proven experience in NLP, information retrieval and search.
  • 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 understanding of design of experiments, online experimentation approaches, preferably at scale.
  • Experience contributing and/or reviewing research for top conferences and publications.
  • Master's, or equivalent experience, in NLP, Machine Learning, Statistics, 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.
Preferred Qualifications
  • PhD, or equivalent experience, in NLP, Machine Learning, Deep Learning, Large Language Model, Reinforcement Learning or related field with experience building production systems or have equivalent experience working with large data science / machine learning projects in industry.

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 $216,200 and $324,800, 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.

Learn about accessibility in Apple’s workplace

Learn about reasonable accommodations for job applicants

Apple accepts applications to this posting on an ongoing basis.

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