below marketmost similar roles pay hereabove market
This role pays more than
82%
of similar roles. Most pay
$202,800–$254,750
— the blue band above.
At the midpoint, this role pays about
$255k
versus about
$229k
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
3499 open roles
on FindRole.
Listed pay typically runs
$166,600–$277,600
across 2707 roles with salary data.
The Machine Learning Engineer - Notifications & Personalization joins the core iOS experience team to develop privacy-preserving on-device intelligence. You will train, deploy, and evaluate machine learning models that power intelligent system experiences, including notification summaries, smart stack widgets, and Focus predictions. Daily responsibilities include building model infrastructure, designing evaluation frameworks, and implementing data collection algorithms for privacy-preserved training. You will utilize supervised and unsupervised learning, reinforcement learning, and neural networks using Scikit-Learn, CoreML, and CreateML. Technical requirements include proficiency in Python, Objective-C, Swift, and C++ for on-device inference. You must navigate embedded operating system constraints like memory and power while collaborating with Design and Privacy Engineering. The role solves the challenge of delivering personalized, high-quality features that respect user privacy across the iOS system stack.
What does a Machine Learning Engineer earn in California?
Median $231575 from 194 postings across 35 companies.
Large Language Models (LLMs)
Large Multimodal Models (LMMs)
PyTorch
TensorFlow
JAX
Transformers
Mixture-of-Experts (MoE)
Distributed Data Processing
Computer Vision
Natural Language Processing
Synthetic Data Generation
Production-scale Inference
Data Curation
High Performance Computing
Python
PyTorch
JAX
TensorFlow
Swift
C++
Go
Objective-C
LLM
Multimodal Foundation Models
ML Training Pipelines
On-device ML
Model Optimization
Inference Infrastructure
Python
PyTorch
JAX
TensorFlow
Swift
C++
Go
Objective-C
LLM
Multimodal Foundation Models
ML Training Pipelines
Inference Infrastructure
On-device ML
Model Optimization
Dataset Curation
Sensing Systems