This role pays more than
81%
of similar roles. Most pay
$192,050–$254,750
— the shaded band above.
At the midpoint, this role pays about
$255k
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
1984 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1590 roles with salary data.
As a Machine Learning Engineer, you will join the team responsible for developing next-generation video technology by building the infrastructure, pipelines, systems, and models that power video features across products. You will work at the intersection of machine learning, systems engineering, and performance optimization to design, build, test, and scale training infrastructure, data ingestion and curation pipelines, and inference optimizations. The role requires developing robust, modular code while solving challenges in efficiency, throughput, and computational requirements for production deployment. To succeed, you must be proficient in Python, C++, and PyTorch, with a strong understanding of computer architecture principles and AI tooling like Claude. You will also leverage experience with visual data, vision algorithms, distributed training, and large-scale data pipelines to solve complex technical problems within the video technology domain.
What does a Machine Learning Engineer earn in California?
Median $246394 from 172 postings across 28 companies.
Design and scale machine learning systems including training infrastructure and data ingestion pipelines.
Develop and optimize ML models for production deployment across various Apple products.
Perform power and performance optimization for model inference.
Build robust, modular, and well-tested code using Python, C++, and PyTorch.
Implement custom operations in CUDA or perform low-level GPU kernel optimizations.
Manage large-scale data pipelines and distributed training processes.
Develop vision algorithms specifically tailored for image and video data.
What we're looking for
BS degree and 10+ years of hands-on industry experience building production ML systems with a track record of design/implementation leadership.
Deep familiarity with ML-centric flows including training procedures, dataloaders, profiling, debugging, data handling, model development, and deployment.
Proficiency in Python, C++, and PyTorch.
Familiarity with computer architecture principles.
Proficiency in AI tooling and excellent high-level design skills to build robust, modular, clean, and well-tested code.
MS specializing in ML systems, performance engineering, or a related area (preferred).
Experience implementing custom ops in CUDA or low-level GPU kernel optimization (preferred).
Research experience in ML model design, visual data, distributed training, large-scale pipelines, inference serving, and excellent communication skills (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
Python
C++
PyTorch
CUDA
GPU Kernel Optimization
Machine Learning
Distributed Training
Inference Serving
Data Pipelines
Computer Architecture
3DGS
Vision Algorithms
Model Deployment
Performance Engineering
Machine Learning
Artificial Intelligence
Big Data Analytics
Cloud Computing
Data Engineering
ETL
Data Modeling
Software Development Life Cycle
Data Orchestration
Parallel Processing