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Senior Machine Learning Engineer, Privacy-Preserving Personalization at Apple

Seattle, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

17 Feb, 26

Salary

0.0

Posted On

19 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Privacy, Differential Privacy, Federated Learning, k-Anonymity, Big Data, Data Processing, Data Architecture, Data Security, Data Portability, Software Engineering, Python, Scala, Java, MLOps, Large Language Models

Industry

Computers and Electronics Manufacturing

Description

Imagine shaping how millions of people discover content they love on the App Store, Apple Music, and Apple TV+. Our team is responsible for the intelligence that powers these deeply personal experiences. We are at a pivotal moment, defining the next generation of personalization in the era of Generative AI. Our challenge is unique and profound: how do we deliver state-of-the-art, magical user experiences while upholding our unwavering commitment to user privacy? DESCRIPTION This is not a standard ML role. We are looking for a pioneering engineer to help us invent the future. You will be a foundational member of the team architecting how we manage and learn from data, setting the standard for privacy and compliance across the globe. MINIMUM QUALIFICATIONS BS or MS in Computer Science, Statistics, or a related field, preferably with a focus on machine learning or data privacy. Senior-Level Experience: A proven track record of shipping complex, large-scale machine learning systems to production. Privacy & Compliance Expertise: Deep experience and passion for privacy-preserving techniques (e.g., Differential Privacy, Federated Learning, k-Anonymity) and their practical application. Mastery of Big Data: Expertise in designing and building distributed data processing systems at petabyte scale using technologies like Spark, Flink, Beam, or similar frameworks. Strategic Data Mindset: Demonstrated experience thinking critically about data architecture, including data ontology, data security models, and data portability challenges. PREFERRED QUALIFICATIONS Familiarity with building or integrating agentic systems and Large Language Models (LLMs). Experience with MLOps best practices for model deployment, monitoring, and governance in a regulated environment. A strong background in software engineering, with proficiency in languages like Python, Scala, or Java.

Responsibilities

You will be a foundational member of the team architecting how we manage and learn from data. The role involves defining the next generation of personalization while ensuring user privacy.