This role pays more than
82%
of similar roles. Most pay
$212,132–$260,450
— the shaded band above.
At the midpoint, this role pays about
$270k
versus about
$236k
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
1984 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1590 roles with salary data.
Staff Machine Learning Engineer – Ads Predictions joins the Predictions group to build core machine learning models powering ad predictions and monetization across App Store and News platforms. This role involves designing and implementing models to improve user interaction, click-through rate, and conversion rate while developing retrieval algorithms using classical information retrieval and modern deep learning. The engineer will work with large-scale distributed datasets to identify signals, optimize marketplace outcomes, and explore emerging techniques in Large Language Models, Reinforcement Learning, and representation learning. Key technical requirements include expertise in neural network architectures like Transformers and DNNs, experience with TensorFlow or PyTorch, and proficiency in Python, SQL, Scala, or Java. The role focuses on solving complex problems in ad tech, recommender systems, and web-scale search while ensuring high performance across billions of queries in a privacy-first environment.
What does a Machine Learning Engineer earn in California?
Median $246394 from 172 postings across 28 companies.
Design and implement machine learning models to improve predictions for user interaction, click-through rate, and conversion rates.
Develop and optimize retrieval algorithms using both classical information retrieval techniques and modern deep learning.
Implement advanced modeling techniques including deep neural networks, contextual bandits, multi-task learning, and LLM-based ranking signals.
Analyze large-scale distributed datasets to identify new signals and improve model accuracy and robustness.
Design and execute large-scale experiments to validate new model architectures and learning strategies.
Operationalize emerging technologies in Large Language Models (LLMs) and Reinforcement Learning for ad prediction systems.
What we're looking for
Bachelor's degree or equivalent experience in Computer Science, Machine Learning, AI, Information Retrieval, or a related field.
MS or PhD in Computer Science, Machine Learning, AI, Information Retrieval, or a related field (preferred).
8+ years of experience applying machine learning and statistical modeling at scale, preferably in ad tech, recommender systems, or web-scale search/retrieval.
Deep experience with neural network architectures like Transformers, DNNs, and RNNs using TensorFlow or PyTorch.
Practical understanding of reinforcement learning, explore/exploit strategies, and bandit-based optimization.
Experience working with high-volume data pipelines, A/B testing infrastructure, and performance measurement at scale.
Proficiency in Python and familiarity with SQL, Scala, or Java for production environments.
Strong foundation in information retrieval, including query-document matching, embedding-based ranking, and learning-to-rank algorithms (preferred).