AIML - Sr. Machine Learning Data Engineer, Machine Learning Platform Techn at Apple

Seattle, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

16 Jan, 26

Salary

0.0

Posted On

18 Oct, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Big Data Infrastructure, Dataset Management, Data Governance, Data Lineage Tracking, Compliance Workflows, APIs, SDKs, Cloud Platforms, Container Orchestration, Data Modeling, Schema Evolution, Data Pipelines, MLOps Tools, Privacy-Preserving Technologies, Metadata Management, Versioning

Industry

Computers and Electronics Manufacturing

Description

Join us in building the machine learning platform that enables teams at Apple to build Apple Intelligence and many other intelligent experiences across hardware, software and service products. As a Machine Learning Data Platform Engineer, you'll design and build the scalable dataset management platform that enables teams across Apple to discover, curate, version, share, process, and consume ML datasets with enterprise-grade compliance and governance. We're looking for an engineer with deep expertise in big data infrastructure and a passion for building platforms that make ML practitioners more productive. You'll work at the intersection of large-scale data systems, ML workflows, and data governance. DESCRIPTION In this role, you'll be architecting and building Apple's next-generation ML dataset management platform. This platform enables ML teams across the company to efficiently manage the full lifecycle of datasets, from initial curation and annotation through versioning, model training and evaluation, sharing, and compliance. You'll design scalable infrastructure that supports dataset operations at massive scale while maintaining strong governance guarantees. Your work will include building data lineage tracking systems, implementing automated compliance workflows, creating intuitive APIs and SDKs for dataset access, and ensuring seamless integration with ML training and evaluation pipelines, You'll collaborate with teams building customer-facing ML features across iOS, macOS, and other Apple platforms, as well as compute infrastructure teams and ML framework owners. Your platform work directly enables the ML innovations that millions of customers experience daily. This role offers the opportunity to have broad impact across Apple's ML initiatives and to shape how thousands of ML practitioners build the intelligent experiences our customers love. MINIMUM QUALIFICATIONS Bachelor's degree in Computer Science, related field, or equivalent practical experience. 10+ years building and scaling data infrastructure for petabyte-scale ML workloads with high reliability Deep expertise in modern data technologies (Apache Iceberg, Spark, S3, distributed systems), data modeling, schema evolution, and efficient storage formats (Parquet, Arrow, ORC) Experience building data pipelines that handle diverse ML data types: structured/tabular data, unstructured media (images, video, audio), embeddings, and multimodal datasets Proven track record building dataset management systems including versioning, metadata management, discovery, and integration with production ML training pipelines Experience designing data governance frameworks including lineage tracking, access control, retention policies, and compliance workflows Experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Kubernetes) Strong cross-functional collaboration skills to understand diverse stakeholder needs and articulate technical decisions across ML engineering, data science, legal, and product teams PREFERRED QUALIFICATIONS Hands-on experience curating or managing datasets for production ML models Experience with data cataloging systems, metadata platforms, MLOps tools, or ML training frameworks Knowledge of privacy-preserving technologies and data quality/validation frameworks

Responsibilities

Architect and build Apple's next-generation ML dataset management platform. Collaborate with teams to manage the full lifecycle of datasets while ensuring compliance and governance.