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AIML-Sr. On-Device Machine Learning Engineer, Measurement

Apple

Company : Apple

Location : Seattle, WA, 98127

Posted Date : 15 October 2025

Job Details

AIML-Sr. On-Device Machine Learning Engineer, Measurement

Seattle, Washington, United States Software and Services

Description

We are looking for an experienced on-device machine learning engineer to join our team and help us develop privacy-preserving technologies for measurement and machine learning.In this role you will:

  • Collaborate with cross-functional engineers to design and develop end-to-end measurement systems that meet Apple's industry-leading privacy standards.
  • Operate independently to solve complex problems at the intersection of privacy-preserving technology, applied cryptography and machine learning.
  • Develop data processing systems that enable training and evaluation of generative AI systems while preserving user privacy.
  • Successful candidates must possess a strong technical background and a keen interest or experience in iOS development, particularly in designing privacy-preserving features. Strong interpersonal skills and the ability to influence and build consensus are essential for success in this role. Since you'll be working with various technologies, adaptability to new challenges and systems is a fundamental skill.

    Minimum Qualifications

    • Proficiency in one or more object-oriented programming languages such as C++, Objective-C, Swift.
    • Industry experience of iOS development, or experience of development on other embedded systems.
    • Knowledge of basic privacy, security and cryptographic principles.
    • Ability to analyze system architecture and assess privacy & security impacts and suggest mitigations.
    • Proficiency in problem-solving, coupled with creativity in identifying effective solutions, and the ability to collaborate effectively within a cross-functional team.
    • 2+ years of industry experience with a Bachelor’s degree or equivalent experience in Computer Science or a related technical field.

    Preferred Qualifications

    • Experience with differential privacy, secure multi-party computation, trusted compute environment or private federated learning.
    • Previous experience in developing security- and privacy-oriented protocols or systems, or experience in designing cryptographic algorithms.
    • Experience with training, evaluating, and deploying machine learning models to production.
    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 $139,500 and $258,100, 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.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 .

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