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.
Sr Machine Learning Engineer - ML Data joins the Machine Learning Platform team to design and develop secure, scalable back-end systems that support modeling and experimentation across various advertising teams. This role involves building high-performing infrastructure from the ground up, including feature stores, embedding management for retrieval at scale, and production data pipelines using distributed processing systems. The engineer will manage multi-modal training data generation involving text, images, and structured data while ensuring point-in-time correctness. Key technical requirements include proficiency in PyTorch or TensorFlow, experience with batch and streaming deployments, workflow orchestration, and modern storage formats. The role addresses complex ad network challenges by building machine learning products that prioritize privacy. Candidates should possess expertise in deep learning architectures, model evaluation, and large-scale distributed training within advertising, recommender systems, or information retrieval domains.
What does a Machine Learning Engineer earn in California?
Median $246150 from 187 postings across 29 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 infrastructure to support model evaluation, training, and deployment.
Develop privacy-preserving machine learning solutions that align with company commitments.
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, refresh/retirement policies, and serving at scale.
Solid understanding of the ML lifecycle, including training, evaluation, deployment, and managing train-serve skew and data drift.
Working knowledge of deep learning architectures and frameworks like PyTorch or TensorFlow.
Experience building production data pipelines for ML systems using distributed processing, batch/streaming deployments, and workflow orchestration.
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
Information Retrieval
Recommender Systems
Multi-modal Data
Agentic AI
Machine Learning
PyTorch
TensorFlow
Deep Learning
LLMs
CI/CD
Distributed Training
Data Drift
Reinforcement Learning
Multimodal Models
Agentic AI
Privacy-Preserving ML