below marketmost similar roles pay hereabove market
This role pays more than
79%
of similar roles. Most pay
$213,500–$260,554
— the blue band above.
At the midpoint, this role pays about
$270k
versus about
$237k
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
3497 open roles
on FindRole.
Listed pay typically runs
$174,149–$277,600
across 2702 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 for the App Store and News platforms. The role involves designing and implementing models to improve user interaction, click-through rate, and conversion rate predictions. Key responsibilities include developing retrieval algorithms, optimizing marketplace outcomes, and exploring emerging techniques in Large Language Models, Reinforcement Learning, and representation learning. The candidate will work with large-scale distributed datasets to identify signals and improve model robustness. Required technical skills include deep experience with neural network architectures like Transformers, DNNs, and RNNs using TensorFlow or PyTorch. Proficiency in Python, SQL, Scala, or Java is necessary to manage high-volume data pipelines and production environments within a privacy-first ad tech context.
What does a Machine Learning Engineer earn in California?
Median $235750 from 200 postings across 34 companies.
Design and implement ML models to improve user interaction, click-through rate, and conversion rate predictions.
Develop and optimize retrieval algorithms using classical information retrieval and modern deep learning techniques.
Contribute to core modeling areas including deep neural networks, contextual bandits, and multi-task learning.
Integrate LLM-based ranking signals into existing ad prediction systems.
Analyze large-scale, distributed datasets to identify new signals and improve model robustness.
Design and run large-scale experiments to validate new model architectures and learning strategies.
Scale machine learning models to production environments across high-volume data pipelines.
What we're looking for
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 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.
Bachelor's degree or equivalent experience in Computer Science, Machine Learning, AI, Information Retrieval, or a related field.
MS or PhD, or equivalent experience, in Computer Science, Machine Learning, AI, Information Retrieval, or a related field (preferred).
Great foundation in information retrieval, including query-document matching, embedding-based ranking, and learning-to-rank algorithms (preferred).
Machine Learning
ML Serving
Distributed Systems
GPU Kernels
ONNX Runtime
TensorRT
vLLM
Ray
RPC
Feature Stores
Vector DB
Model Quantization
Tensor Parallelism
Federated Learning
Privacy-Preserving ML
Agile
ML Serving
ONNX Runtime
TensorRT
vLLM
Ray
RPC
GPU Kernels
Federated Learning
Feature Stores
Vector DB
Distributed Systems
Model Quantization
Tensor Parallelism
Inference Optimization
Agile