This role pays more than
92%
of similar roles. Most pay
$191,537–$254,750
— the shaded band above.
At the midpoint, this role pays about
$270k
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
2202 open roles
on FindRole.
Listed pay typically runs
$175,000–$278,800
across 1802 roles with salary data.
Machine Learning Engineer - Product Marketing Customer Analytics joins the Product Marketing Customer Analytics team to provide predictive analytics regarding customer product and services engagement. This role involves translating business requirements into modeling tasks, developing scalable machine learning algorithms to uncover actionable insights, and designing end-to-end pipelines from feature engineering to model serving. The engineer will manage projects through all phases, including data quality, predictive modeling, and deployment on high-volume datasets using GPU clusters or distributed CPU environments. Key technical skills include Python, Spark, TensorFlow, PyTorch, SQL, Oracle, Hadoop, and Snowflake. The role focuses on solving complex, non-routine prediction problems by applying advanced techniques like regression, clustering, and classification to understand customer behavior. Candidates must be proficient in MLOps frameworks, including feature stores and CI/CD integration, to operationalize models within a fast-changing environment.
What does a Machine Learning Engineer earn in California?
Median $241375 from 174 postings across 27 companies.
Translate product requirements into specific modeling and engineering tasks.
Develop scalable ML algorithms to analyze customer behavior and provide actionable insights.
Design and implement end-to-end machine learning pipelines from feature engineering to model serving.
Optimize deep learning and traditional ML models on high-volume datasets using GPU or distributed CPU clusters.
Manage the full ML lifecycle including data quality, predictive modeling, visualization, and deployment.
Solve complex, non-routine prediction problems using advanced machine learning methods.
Build and prototype analysis pipelines to provide insights at scale.
Collaborate with infrastructure partners to operationalize models and maintain robust production systems.
What we're looking for
Graduate degree required in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or a related field.
8+ years of hands-on programming skills for large-scale data processing.
Excellent understanding of analytical methods and machine learning algorithms including regression, clustering, classification, optimization, and other advanced techniques (preferred).
8+ years of experience building and scaling predictive models across distributed systems, production model hosting, and end-to-end performance optimization (preferred).
8+ years of programming skills in Python and/or Spark for large-scale data processing and maintaining high-throughput ML pipelines (preferred).
Proficiency with advanced deep learning frameworks like TensorFlow or PyTorch and experience designing scalable ML platforms (preferred).
Solid technical database and data modeling knowledge including Oracle, Hadoop, and Snowflake (preferred).
Strong communication skills to explain complex technical topics to both technical peers and non-technical stakeholders (preferred).
LLM
PyTorch
TensorFlow
JAX
Transformers
MoE
Computer Vision
Natural Language Processing
Distributed Data Processing
Machine Learning
Deep Learning
OCR
Synthetic Data Generation
Data Curation
High Performance Computing
Machine Learning
Python
Java
C++
Spark
SQL
Snowflake
Hadoop
TensorFlow
Keras
PyTorch
Generative AI
RAG
Transformer Architecture
Deep Learning
Reinforcement Learning
Recommender Systems
Natural Language Processing
Distributed Systems