AIML- Staff ML Engineer, Machine Learning Platform Technologies

AIML- Staff ML Engineer, Machine Learning Platform Technologies

San Francisco Bay Area, California, United States Machine Learning and AI

Do you want to make Apple products more intelligent for our users? The AIML Information Intelligence teams are building groundbreaking technology for algorithmic search and recommendation, machine learning, natural language processing, and artificial intelligence. The features we build are redefining how hundreds of millions of people use their computers and mobile devices to search and find what they are looking for. Apple's universal search engine powers search features across a variety of its products, including Siri, Spotlight, Safari, Music, App Store, Video, and many more. We are searching for Backend Infrastructure Engineers to build scalable, highly available, and robust platforms. Build, evolve, and scale state-of-the‑art machine learning system infrastructure powering Apple's Search Platforms. Be a key engineer contributing to the design, development, and operation of the large‑scale infrastructure systems. Work with other Machine learning / Deep learning researchers and backend engineers to implement scalable solutions to tackle sophisticated problems.

Description

We design and build infrastructures to support features that empower billions of Apple search users. Our team processes trillions of links to find the best content to surface to users through search. We also analyze pages to extract critical features for indexing, ranking. We apply statistical analysis to improve crawling, extraction quality, web and knowledge graph data, link selection, freshness, retrieval performance, and many others. You'll have the opportunity to work with large scale systems with trillions of rows and many petabytes of data and incredible complexity.

Minimum Qualifications

• BS, MS, Ph.D. in a related field, or equivalent experience
• Experience Machine learning
• Expertise in large-scale information retrieval, high-volume data mining, and contemporary machine learning methodologies.
• Strong background in computer science : algorithms and data structures
• Experience with backend development, code performance tuning and testing
• Experience with cloud services and Kubernetes
• 5+ years of experience leading and driving complex, ambiguous projects.

Preferred Qualifications

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 $212,000 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.

Apple accepts applications to this posting on an ongoing basis.

#J-18808-Ljbffr