below marketmost similar roles pay hereabove market
This role pays more than
52%
of similar roles. Most pay
$198,900–$254,750
— the blue band above.
At the midpoint, this role pays about
$231k
versus about
$227k
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
3515 open roles
on FindRole.
Listed pay typically runs
$174,149–$277,600
across 2718 roles with salary data.
The Senior Machine Learning Engineer - ML Platform joins the Apple Ads Machine Learning Platform team to design and develop secure, scalable back-end systems. This role involves building world-class platform capabilities that enable Ads teams to improve and scale machine learning features, models, and applications. Key responsibilities include developing ML infrastructure, frameworks, services, SDKs, APIs, and automation UIs while managing the full machine learning lifecycle, including training, evaluation, deployment, and serving. The candidate must utilize deep learning architectures and training frameworks such as PyTorch or TensorFlow. The work focuses on solving unique ad network challenges, including distributed training, low-latency serving, and privacy-preserving machine learning for advertising, recommender systems, and information retrieval. The role requires expertise in CI/CD, data drift, and model evaluation.
What does a Machine Learning Engineer earn in California?
Median $235750 from 201 postings across 34 companies.
Design and develop secure, scalable back-end systems for machine learning platforms.
Build ML infrastructure, frameworks, and services used by multiple internal teams.
Develop SDKs, APIs, and automation UIs to improve the developer experience for ML practitioners.
Implement automation and CI/CD pipelines for production machine learning systems.
Manage the full ML lifecycle including training, evaluation, deployment, and serving/inferencing.
Optimize large-scale models for low-latency serving and distributed training.
Build infrastructure for diverse model types including LLMs, multimodal models, and reinforcement learning.
Develop privacy-preserving machine learning products that meet Apple's privacy commitments.
What we're looking for
Experience writing mission-critical code for production machine learning systems.
Experience building ML infrastructure, frameworks, services, SDKs, APIs, and automation UIs.
Proven experience building automation and CI/CD.
Solid understanding of the ML lifecycle, including training, evaluation, deployment, and serving/inferencing.
Working knowledge of deep learning architectures and training frameworks such as PyTorch or TensorFlow.
Prior experience applying ML at scale in advertising, recommender systems, or information retrieval.
PhD in Computer Science with 3+ years of engineering and 5+ years of ML experience; or MS with 6+ years of engineering and 5+ years of ML experience; or BS with 7+ years of engineering and 5+ years of ML experience.
Experience in advertising, distributed training, LLMs, foundation models, or privacy-preserving ML (preferred).
Machine Learning
PyTorch
TensorFlow
CI/CD
Distributed Training
LLMs
Deep Learning
API
Reinforcement Learning
Multimodal Models
Information Retrieval
Recommender Systems
Privacy-Preserving ML
PyTorch
TensorFlow
CI/CD
LLMs
Distributed Training
Deep Learning
Machine Learning
API
SDKs
Reinforcement Learning
Multimodal Models
Data Drift
Privacy-Preserving ML
Agentic AI