This role pays more than
85%
of similar roles. Most pay
$177,200–$249,750
— the shaded band above.
At the midpoint, this role pays about
$265k
versus about
$213k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
638 open roles
on FindRole.
Listed pay typically runs
$171,600–$272,100
across 505 roles with salary data.
As a Staff Machine Learning Engineer on Apple’s Cloud AI Platform team, you will design and build the platform supporting large-scale model builds by creating robust data ingestion pipelines, versioning systems, and governance frameworks for petabyte-scale datasets. Your daily tasks include developing Python SDKs and core data libraries to streamline dataset access and transformation across all stages of model development, optimizing high-throughput data loading primitives for GPU fleets, and building distributed data pipelines with Spark, Daft, and Rust-based systems. You will also ensure legal compliance and privacy controls while integrating platform components tightly with ML frameworks like PyTorch, JAX, and TensorFlow. The role requires expertise in machine learning infrastructure, large-scale distributed systems, and modern generative techniques, along with extensive programming experience in Java, Python, or Go.
Design and build scalable data ingestion and versioning systems for petabyte-scale datasets.
Develop Python SDKs and core libraries to facilitate model-ready dataset access across stages.
Construct high-throughput data primitives to feed large GPU fleets efficiently.
Build distributed data pipelines using Spark, Daft, and Rust-based systems for data preparation.
Ensure legal compliance, privacy controls, and end-to-end data lineage in platform governance.
What we're looking for
Strong foundation in machine learning with hands-on experience across data preparation, pipeline development, experimentation, evaluation, and deployment.
Expertise in building and running large-scale distributed systems for data ingestion, transformation, and large-scale data preparation.
Familiarity with modern generative techniques such as transformers, diffusion models, and retrieval-augmented generation.
Proven experience building and delivering data and machine learning infrastructure in real-world production environments.
Experience configuring, deploying, and troubleshooting large-scale production environments for ML frameworks like PyTorch, JAX, or TensorFlow.