5 days ago
Responsibilities
- Set the technical direction and architecture for Apple’s ML data and infrastructure platform.
- Own the architecture and resolution of complex system-level problems across distributed data and ML infrastructure.
- Build and operate ingestion, versioning, lineage, governance, and high-throughput data-loading systems for ML training fleets.
- Design infrastructure supporting ML training, inference, generative AI, embeddings, feature platforms, and efficient inference.
- Perform systems and performance engineering for I/O-bound workloads, including zero-copy, memory mapping, asynchronous I/O, and high-throughput object storage.
- Choose and operate scalable columnar and lakehouse data formats.
- Drive technical direction and delivery across multiple teams and products.
- Mentor and elevate engineers while designing highly available and easy-to-use systems.
Requirements
- At least 10 years of work experience in machine learning infrastructure, distributed data systems, or a related field.
- At least 10 years of experience building and shipping large-scale data or ML infrastructure and platforms in production.
- Extensive experience architecting and delivering distributed data or ML infrastructure used by multiple production teams or products.
- A track record of setting technical direction and driving cross-team delivery.
- Strong Python and a systems language; Rust is strongly preferred, with C++ or Go acceptable.
- Hands-on performance engineering experience with I/O-bound workloads, including Arrow, zero-copy, memory mapping, asynchronous I/O, and high-throughput object storage.
- Deep familiarity with Parquet, Iceberg, Delta, or Lance and the judgment to select formats at scale.
- Strong knowledge of end-to-end ML workflows and how training and inference consume data.
- Familiarity with transformers, diffusion, retrieval-augmented generation, and fine-tuning.
- Experience designing highly available systems and mentoring engineers.
- Experience defining data or ML platform architecture adopted across an organization.
- Experience with PyTorch, JAX, or TensorFlow data-loading and dataset-access layers.
- Experience with Ray Data, NVIDIA DALI, WebDataset, or Mosaic StreamingDataset.
- Experience feeding data to GPU or TPU fleets at scale.
- Experience with DataHub, OpenLineage, Unity Catalog, or equivalent data lineage and governance systems.
- Contributions to or operational experience with Spark, Daft, Polars, or DuckDB internals.
- Experience with Docker and Kubernetes.
- Bachelor’s, master’s, or doctoral degree in Computer Science, Computer Engineering, or equivalent practical experience.
Tech Stack
Categories
Data EngineeringML Engineering
About Apple
Apple designs and sells consumer electronics, software, and services for consumers and professionals worldwide, including iPhone, Mac, iPad, Apple Watch, and AirPods, plus platforms like iOS/macOS and services such as the App Store, iCloud, Music, and TV+. Its business combines device sales with services and subscriptions and in-house silicon design. Founded in 1976, Apple is headquartered in Cupertino, California, and trades on NASDAQ as AAPL.