This role pays more than
52%
of similar roles. Most pay
$192,050–$254,750
— the shaded band above.
At the midpoint, this role pays about
$231k
versus about
$223k
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.
As a Sr Machine Learning Engineer - ML Data, you will join the Apple Ads Machine Learning Platform team to design and develop secure, scalable back-end systems and infrastructure. You will collaborate with engineers and scientists to build world-class platform capabilities that enable internal teams to scale machine learning features, models, and applications while upholding privacy commitments. Your daily work involves building feature stores, managing embeddings for retrieval at scale, and developing production data pipelines using distributed processing systems. You will manage the full machine learning lifecycle, including training, evaluation, and deployment of models across multi-modal data types like text and images. Required skills include proficiency in PyTorch or TensorFlow, experience with batch and streaming deployments, workflow orchestration, and modern storage formats to solve complex challenges within advertising and recommender systems.
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 the machine learning platform.
Build and operate feature stores and related ML data infrastructure for production models.
Manage embedding lifecycles including generation, versioning, storage, and retrieval at scale.
Develop production data pipelines using distributed processing systems to ensure high performance.
Create training data for multi-modal content including text, images, and structured data.
Implement batch and streaming deployments with workflow orchestration and modern storage formats.
Build tools and automation for CI/CD, debugging, and performance tuning of ML systems.
Develop privacy-preserving machine learning solutions to meet corporate security standards.
What we're looking for
Experience writing mission-critical code for production machine learning systems and building ML infrastructure, frameworks, or services used by multiple teams.
Experience building or operating feature stores or comparable ML data infrastructure serving production models.
Experience with embedding management including generation, versioning, and serving at scale.
Solid understanding of the ML lifecycle, including training, evaluation, deployment, and monitoring for data drift and train-serve skew.
Working knowledge of deep learning architectures and frameworks such as PyTorch or TensorFlow.
Experience building production data pipelines using distributed processing systems, workflow orchestration, and modern storage formats.
Strong data modeling skills with a focus on system quality, performance tuning, and CI/CD.
PhD in Computer Science (3+ years engineering, 5+ years ML); MS (6+ years engineering, 5+ years ML); or BS (7+ years engineering, 5+ years ML).
PyTorch
TensorFlow
Machine Learning
Deep Learning
LLM
Feature Stores
Data Pipelines
CI/CD
Workflow Orchestration
Information Retrieval
Recommender Systems
Multi-modal Data
Machine Learning
PyTorch
TensorFlow
Deep Learning
LLMs
CI/CD
Distributed Training
Data Drift
Reinforcement Learning
Multimodal Models
Agentic AI
Privacy-Preserving ML
Machine Learning
Natural Language Processing
PyTorch
TensorFlow
JAX
Python
Go
Java
C++
Computer Vision
Deep Learning
Content Moderation
Adversarial Training
Search Ranking
Information Retrieval
Multimodal Content Filtering