below marketmost similar roles pay hereabove market
This role pays more than
84%
of similar roles. Most pay
$198,000–$251,400
— the blue band above.
At the midpoint, this role pays about
$255k
versus about
$225k
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.
JOB TITLE: Machine Learning Engineer
The Machine Learning Engineer joins the Music ML team within Apple Services Engineering to build and improve features driving Apple Music personalisation. This role involves designing and running customer-facing recommendation services for features like Home, New, Radio, and Personal Mixes. You will perform data analysis, manage large-scale offline pipelines, and handle machine-learned model training and inference to provide real-time experiences. Key responsibilities include developing complex systems that integrate diverse data sources with low latency, tuning performance for throughput, and deploying systems globally for resiliency. You will prototype algorithm changes, launch A/B tests, and collaborate with researchers. Required skills include experience with scalable recommendation systems, concurrency, algorithms, and object-oriented programming. Preferred technical expertise includes proficiency in Java, C++, and Python to engineer modern ML-driven pipelines and APIs.
What does a Machine Learning Engineer earn in California?
Median $235750 from 200 postings across 34 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
C++
PyTorch
CUDA
GPU Kernel Optimization
Distributed Training
Data Pipelines
Inference Serving
Computer Architecture
Model Deployment
Data Ingestion
Profiling
Debugging
Claude