1 day ago
Responsibilities
- Build scalable data and machine learning infrastructure for Apple’s ML engineers and researchers.
- Develop systems supporting the end-to-end ML workflow from data preparation and experimentation through evaluation and deployment.
- Build ML data and feature platforms supporting embeddings generation, evaluation, approximate nearest-neighbor search, multimodal workloads, and generative AI applications.
- Design, build, maintain, configure, deploy, and troubleshoot large-scale, highly available distributed systems.
- Support fine-tuning workflows, model optimization, efficient inference, and production model serving.
- Improve data loading, storage access, performance, lineage, governance, and orchestration for ML workloads.
- Collaborate with engineers and researchers to take ML and generative AI ideas from prototype to production.
Requirements
- Strong foundation in machine learning and hands-on experience across data preparation, pipeline development, experimentation, evaluation, and deployment.
- Expertise building and operating large-scale distributed systems and production ML infrastructure.
- Familiarity with transformers, diffusion, retrieval-augmented generation, fine-tuning, model optimization, and scalable inference.
- Experience configuring, deploying, and troubleshooting large-scale production environments and designing highly available, easy-to-use systems.
- Extensive programming experience in Java, Python, or Go.
- Strong verbal and written collaboration and communication skills, with comfort working in ambiguous and rapidly evolving technical areas.
- Bachelor’s, master’s, or doctoral degree in Computer Science or Computer Engineering, or equivalent practical experience.
- Preferred experience with PyTorch, JAX, TensorFlow, Parquet, Iceberg, Delta, Lance, Ray Data, NVIDIA DALI, WebDataset, Mosaic StreamingDataset, Arrow, DataHub, OpenLineage, Unity Catalog, Spark, Daft, Polars, DuckDB, Docker, or Kubernetes.
- Preferred experience with performance engineering for I/O-bound workloads, zero-copy, memory mapping, asynchronous I/O, high-throughput object storage at GPU scale, and ML data governance.
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.