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Staff ML Infrastructure Engineer

Apple· Software and Services
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Cupertino, California$184.7k - $324.8kFull-timePosted 3w ago

About the role

Join a team at the forefront of ML infrastructure and generative AI, building robust systems that connect scalable data pipelines with advanced ML workflows to accelerate real-world AI applications. The role focuses on the data layer and platform that powers large-scale model training and deployment, including ingestion, versioning, lineage, governance, and high-throughput data loading for extensive GPU/TPU fleets.

Responsibilities

  • Own the architecture of the platform behind Apple's largest model builds: define how ingestion, immutable versioning, lineage, and governance work across structured, unstructured, and multimodal data at petabyte scale, so every model run is reproducible from a versioned dataset.
  • Set the technical direction for high-throughput data delivery to Apple's largest GPU and TPU fleets: define the data access and loading architecture that keeps training compute-bound, not I/O-bound.
  • Make the hard system-level and format calls that the whole platform inherits, columnar and lakehouse strategy, the dataset abstraction spanning structured and multimodal data, the shape of the SDK and core libraries, backed by design and proof, not just opinion.
  • Drive technical direction and influence across the platform and partner teams (data, embeddings, features, research), and define the interfaces and contracts between them.
  • Raise the technical bar across the team: mentor senior engineers, lead design reviews, and be the escalation point for the problems no one else can crack.
  • Partner with research and product leadership to shape the platform roadmap for next-generation workloads: foundation models, multimodal data, and retrieval-augmented systems.
  • Drive efficiency, reliability, and automation across the data plane and control plane that power Apple's ML fleet.

Qualifications

  • 10+ years of work experience in machine learning infrastructure, distributed data systems, or a related field.
  • 10+ years of experience building and shipping large-scale data or ML infrastructure and platforms in production.
  • Extensive experience architecting and delivering large-scale distributed data or ML infrastructure that multiple teams or products depend on in production.
  • A track record of setting technical direction and driving it to delivery across teams, not just within a single component.
  • Deep systems engineering: strong Python plus a systems language (Rust strongly preferred; C++ or Go acceptable), and hands-on performance engineering for I/O-bound workloads (Arrow, zero-copy, memory mapping, async I/O, high-throughput object storage).
  • Deep familiarity with columnar and lakehouse formats (Parquet, Iceberg, Delta, or Lance) and the judgment to choose between them at scale.
  • Strong working knowledge of the end-to-end ML workflow and how training and inference consume data, enough to architect data systems that serve them.
  • Familiarity with modern ML and generative techniques (transformers, diffusion, retrieval-augmented generation, fine-tuning) at the level needed to design for those consumers.
  • Demonstrated ability to design highly available, easy-to-use systems and to mentor and elevate the engineers around you.
  • Strong collaboration and communication, with the ability to align multiple teams around a technical direction.
  • B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience.

Skills

Required:PythonRustArrowParquet/Iceberg/Delta/LanceDistributed data systemsGPU/TPU data feeding
Nice to have:C++/GoData governance / lineagePyTorch/JAX/TensorFlow

Benefits

  • Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and education-related reimbursements — including tuition.
  • Discretionary bonuses or commission payments as well as relocation may be available.
  • Apple shareholder opportunities through discretionary employee stock programs and Employee Stock Purchase Plan.
Last updated Oct 2, 2026. Interested? Apply on the company site.

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