This role pays more than
80%
of similar roles. Most pay
$172,500–$254,750
— the shaded band above.
At the midpoint, this role pays about
$255k
versus about
$214k
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
3553 open roles
on FindRole.
Listed pay typically runs
$165,800–$277,600
across 2740 roles with salary data.
As a Staff ML Infrastructure Engineer, you will join the AI Platform team to architect and lead the technical direction of the data layer supporting large-scale model training. You will solve complex system-level problems involving ingestion, immutable versioning, lineage, and governance for structured, unstructured, and multimodal data at petabyte scale. Your role involves designing high-throughput data delivery systems for GPU and TPU fleets to ensure compute remains prioritized over I/O. You will work with Python and a systems language like Rust, C++, or Go, while utilizing technologies such as Arrow, Parquet, Iceberg, Delta, or Lance. The work focuses on the infrastructure required for generative AI, including embeddings, inference, and retrieval-augmented systems. You will mentor engineers and define core libraries and SDKs to streamline the end-to-end machine learning lifecycle from experimentation to production.
Architect the platform for ingestion, versioning, lineage, and governance of petabyte-scale multimodal data.
Design high-throughput data delivery architectures to ensure GPU and TPU fleets remain compute-bound rather than I/O-bound.
Define system-level standards for columnar and lakehouse strategies, dataset abstractions, and core SDK libraries.
Establish technical interfaces and contracts between internal teams including data, embeddings, features, and research.
Mentor senior engineers and serve as the primary escalation point for complex system-level problems.
Partner with leadership to develop the roadmap for foundation models and retrieval-augmented systems.
Improve efficiency, reliability, and automation across both the data plane and control plane of the ML fleet.
What we're looking for
10+ years of 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.
Proficiency in Python and a systems language (Rust preferred; C++ or Go acceptable) with performance engineering for I/O-bound workloads.
Deep familiarity with columnar and lakehouse formats like Parquet, Iceberg, Delta, or Lance.
Strong knowledge of end-to-end ML workflows, including modern generative techniques like transformers and diffusion models.
B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience.
Python
Go
C++
Kubernetes
PyTorch
JAX
TensorFlow
Ray
vLLM
Pathways
GPU
TPU
Distributed Systems
Container Orchestration
Performance Engineering
Cloud Infrastructure
Model Serving
Inference
Fine-tuning
Python
Go
C++
Kubernetes
PyTorch
JAX
TensorFlow
Ray
vLLM
Pathways
GPU
TPU
Distributed Systems
Container Orchestration
Performance Engineering
Cloud Infrastructure
Model Serving
Inference
Fine-tuning