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# Staff ML Infrastructure Engineer

- **Company:** Apple Inc.
- **Location:** Cupertino, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Systems Engineering, Big Data, C++ (Programming Language), Computer Engineering, Data Files, Data Systems, Distributed Data Store, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Parquet, Pytorch, Retrieval-Augmented Generation, Apache Spark, Containerization, Kubernetes, Information Technology, Data Lineage, Machine Learning Operations, Stable Diffusion, Docker
- **Published:** October 12, 2026
- **Apply:** https://www.juju.com/job/1_apple2-200674140

## About the Role

As a Staff ML Infrastructure Engineer, you will set the technical direction for that platform and own its hardest system-level problems, the architecture other engineers and teams build on. **Minimum 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. **Preferred Qualifications** + Experience defining data or ML platform architecture that was adopted across an organization. + Deep experience with the data-loading
and dataset-access layer of a modern ML framework (PyTorch, JAX, or TensorFlow). + Distributed data-loading frameworks for ML: Ray Data, NVIDIA DALI, WebDataset, or Mosaic StreamingDataset. + Experience feeding data to GPU or TPU fleets at scale and keeping them saturated. + Data lineage and governance systems: DataHub, OpenLineage, Unity Catalog, or equivalent. + Contributions to or operational experience with Spark, Daft, Polars, or DuckDB internals. + Containerization and orchestration (Docker, Kubernetes).