This role pays more than
66%
of similar roles. Most pay
$179,968–$251,009
— the shaded band above.
At the midpoint, this role pays about
$242k
versus about
$215k
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
1995 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1612 roles with salary data.
As a Senior Machine Learning Engineer on the Cloud AI Platform team, you will build and operate the infrastructure powering large-scale model builds and generative AI applications. You will design systems for ingestion, immutable versioning, and governance of multimodal data at petabyte scale while developing Python SDKs and core data libraries to streamline the machine learning lifecycle. Your daily work involves building high-throughput data pipelines using Spark, Daft, and Rust-based systems to feed large GPU fleets. You will optimize platform components for integration with PyTorch, JAX, and TensorFlow to ensure seamless dataset access. The role focuses on solving complex problems in data lineage, inference efficiency, and automated infrastructure for foundation models. Required skills include expertise in distributed systems, Python, Java, or Go, along with experience in fine-tuning workflows and large-scale production environments.
What does a Machine Learning Engineer earn in Washington?
Median $241750 from 67 postings across 16 companies.
Design and build infrastructure for petabyte-scale data ingestion, versioning, and lineage across multimodal datasets.
Develop Python SDKs and core libraries to help ML engineers transform and load model-ready data.
Build high-throughput data loading primitives to ensure GPU workloads remain compute-bound rather than I/O-bound.
Operate distributed data pipelines using Spark, Daft, and Rust-based systems for large-scale preparation.
Optimize platform components for seamless integration with PyTorch, JAX, and TensorFlow frameworks.
Implement governance features including legal terms enforcement, privacy controls, and end-to-end data lineage.
Support next-generation workloads such as foundation models, multimodal data, and retrieval-augmented systems.
Diagnose and automate complex issues across the stack to maximize system uptime and throughput.
What we're looking for
B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience.
Strong foundation in machine learning with hands-on experience across the end-to-end ML workflow including data preparation, experimentation, and deployment.
Expertise in building and running large scale distributed systems.
Proven experience building and delivering data and machine learning infrastructure in real-world production environments.
Experience designing, building, and maintaining scalable, highly available systems that prioritize ease of use.
Extensive programming experience in Java, Python, or Go.
Familiarity with modern generative techniques, fine-tuning workflows, model optimization, and large scale production environment troubleshooting.
Proficiency with PyTorch, JAX, TensorFlow, columnar formats, distributed data loading frameworks, or containerization technologies (preferred).
Machine Learning
Computer Vision
C++
Python
PyTorch
TensorFlow
Deep Learning
LiDAR
Multi-sensor Fusion
TensorRT
Transformers
Data Pipelines
Model Optimization
Real-time Inference
NVIDIA DRIVE AV