This role pays more than
51%
of similar roles. Most pay
$197,981–$254,750
— the shaded band above.
At the midpoint, this role pays about
$231k
versus about
$226k
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
2305 open roles
on FindRole.
Listed pay typically runs
$175,000–$280,000
across 1873 roles with salary data.
Sr Machine Learning Engineer - ML Platform joins the Apple Ads Machine Learning Platform team to design, develop, and build world-class platform capabilities that enable internal teams to improve and scale machine learning features, models, and applications. This role involves building secure, scalable back-end systems, infrastructure for diverse model development use cases including LLMs, multimodal models, classical ML, and reinforcement learning, as well as developing SDKs, APIs, and automation UIs. The candidate will work with PyTorch or TensorFlow to manage the full machine learning lifecycle, including training, evaluation, deployment, and serving. Key responsibilities include building CI/CD pipelines and optimizing large-scale models for low-latency serving. The role addresses technical challenges in advertising, recommender systems, and information retrieval while ensuring privacy-preserving machine learning within a high-performance ad network environment to deliver optimized content to consumers.
What does a Machine Learning Engineer earn in New York?
Median $231150 from 51 postings across 14 companies.
Design and develop secure, scalable back-end systems for machine learning features.
Build infrastructure, frameworks, and services used by multiple internal teams.
Develop SDKs, APIs, and automation UIs to improve the developer experience for ML practitioners.
Implement CI/CD pipelines and automated workflows for production machine learning systems.
Manage the full ML lifecycle including training, evaluation, deployment, and serving.
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 solutions to meet corporate data commitments.
What we're looking for
Experience writing mission-critical code for production machine learning systems.
Experience building ML infrastructure, frameworks, or services used by multiple teams.
Experience building SDKs, APIs, and automation UIs for development teams.
Proven experience building automation and CI/CD.
Solid understanding of the ML lifecycle including training, evaluation, deployment, and serving.
Working knowledge of deep learning architectures and training frameworks like PyTorch or TensorFlow.
PhD in Computer Science with 3+ years of engineering and 5+ years of ML experience; 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).
PyTorch
TensorFlow
Machine Learning
Deep Learning
LLM
Feature Stores
Data Pipelines
CI/CD
Information Retrieval
Recommender Systems
Multi-modal Data
Agentic AI
PyTorch
TensorFlow
Machine Learning
Deep Learning
LLM
Feature Stores
Data Pipelines
CI/CD
Workflow Orchestration
Information Retrieval
Recommender Systems
Multi-modal Data