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Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10000+ employees
Industry: Technology, Information Technology, Software, Consumer Goods & Services

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The Apple Data Platform team powers the data analytics, exploration, and feature engineering that fuel Siri, Search, Music, Maps, i Cloud, and many other beloved products across the Apple ecosystem. Our mission is to provide engineers and data scientists with an innovative, reliable, secure, and user-friendly platform for ingesting, storing, processing, and interacting with data-ultimately enabling teams to derive insights that drive product success.

Apple Batch is a fully managed platform within the Apple Data Platform that supports large-scale batch and ML workloads across Apple data centers and AWS/GCP. It orchestrates containerized workloads such as Spark, Ray, and LLM batch inference using Yuni Korn/Kueue for advanced multi-cluster scheduling. The platform delivers org/team quota management, automatic node repair, end-to-end observability, strong security, and granular cost reporting. As we scale Apple Batch across all Data and AI services and Apple-wide ADP customers, we are seeking a Staff/Architect-level technical lead to shape its evolution and drive platform adoption across the company.

Description

Apple Batch, a core platform within the Apple Data/AI ecosystem, enables large-scale scheduling, orchestration, efficiency, and observability for AI and Data workloads across Apple data centers and public cloud providers. We are looking for a Staff/Architect-level technical lead to drive the next generation of the platform and scale it across all Data and AI services and Apple-wide ADP customers.","responsibilities":"Lead the architecture, design, and roadmap for Apple Batch, ensuring scalability, performance, and reliability at massive enterprise scale.

Drive the scheduling and orchestration strategy for Data and AI workloads on Kubernetes and related technologies.

Partner with engineering, SRE, product, and customer teams to deliver highly observable, efficient, and secure production systems.

Collaborate closely with internal customers to guide adoption, gather requirements, and deliver impactful platform capabilities.

Provide technical leadership in areas such as multi-cluster scheduling, workload isolation, autoscaling, and cost optimization.

Preferred Qualifications

Open-source contributions to scheduling/orchestration technologies such as Apache Yuni Korn, Kueue, or similar systems.

Experience using GenAI technologies to improve developer productivity, streamline engineering processes, and accelerate team execution.

Minimum Qualifications

Demonstrated expertise in large-scale scheduling/orchestration for Data and AI workloads on Kubernetes, Slurm, or similar platforms.

Strong proficiency with cloud-native infrastructure across multi-cloud environments including AWS, GCP, and on-prem systems.

Deep knowledge of cluster-scheduling technologies such as Kueue, Apache Yuni Korn, or related ecosystems.

Experience or strong knowledge in GPU scheduling, accelerator-aware placement, and optimization algorithms for large-scale AI/ML workloads.

Proven experience designing and operating large-scale distributed systems with a deep focus on observability, scalability, and reliability.

Demonstrated ability to mentor engineers, influence architectural direction, drive cross-functional alignment, and lead complex platform migrations or adoption at org-wide scale.

Bachelors/Masters/PhD in Computer Science or related field.

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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Client-provided location(s): Cupertino, CA

Job ID: apple-200640192-0836_rxr-660

Employment Type: OTHER

Posted: 2026-01-11T19:10:56
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