below marketmost similar roles pay hereabove market
This role pays more than
85%
of similar roles. Most pay
$211,200–$254,750
— the blue band above.
At the midpoint, this role pays about
$270k
versus about
$233k
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
3532 open roles
on FindRole.
Listed pay typically runs
$174,149–$277,600
across 2761 roles with salary data.
The Staff Machine Learning Engineer joins the Commerce & Growth Intelligence team to build systems integrating machine learning capabilities into customer-facing products. This role involves leading engineering projects across the AI/ML stack, specifically focusing on model inference, shared ML infrastructure, and distributed ML workloads. You will build inference systems for product workloads, develop services and APIs for model execution, and scale fine-tuning and training workflows. Key responsibilities include designing interfaces between components, building data-processing workflows, and connecting ML systems with product software. Required skills include expertise in distributed computing, ML infrastructure, or data systems, alongside proficiency in programming and system design. The role utilizes technologies such as Ray, Ray Serve, ONNX Runtime, Core ML, Spark, Flink, Go, Swift, and Python to solve performance and reliability constraints.
What does a Machine Learning Engineer earn in California?
Median $235750 from 194 postings across 31 companies.
Build inference systems and APIs for model execution, including request routing, batching, and scaling.
Develop shared ML infrastructure by adopting and extending open-source and internal technologies.
Scale fine-tuning and training workflows by improving distributed job execution and accelerator utilization.
Build distributed data-processing workflows to transform large datasets into reproducible inputs for ML.
Design and implement interfaces between inference services, backend applications, and on-device components.
Translate ambiguous requirements into technical architecture and implementation plans for production delivery.
Diagnose issues across data, model execution, and application boundaries while mentoring other engineers.
What we're looking for
Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or Electronic Engineering and 12+ years of relevant work experience.
Experience shipping production ML systems or ML-powered applications with ownership of significant architecture and implementation decisions.
Deep expertise in at least one area: model inference, distributed computing, ML infrastructure, or data systems for ML.
Strong programming and software engineering skills including system design, API design, testing, and debugging complex software.
Practical experience integrating ML models into software systems and understanding their execution and deployment requirements.
Track record of leading technical initiatives across teams, resolving architectural tradeoffs, and delivering working systems.
Experience investigating performance and reliability problems and driving improvements based on measured results.
Master’s degree in Computer Science, Computer Engineering, Information Systems, or Electronic Engineering and 8+ years of relevant work experience (preferred).
Machine Learning
Model Inference
Distributed Computing
ML Infrastructure
Python
Go
Swift
Ray
Ray Serve
ONNX Runtime
Core ML
Spark
Flink
LangGraph
Model Context Protocol
API Design
System Design
Data Processing
Machine Learning
Python
SQL
Spark
Flink
AWS
SageMaker
CI/CD
Distributed Systems
Real-time Inference
Feature Store
Data Governance
System Design
Model Evaluation
Data Lake
Python
LLMs
Deep Learning
Graph Neural Networks
NLP
Machine Learning
Information Retrieval
Data Mining
Statistics
Gradient-Boosted Trees
Regression
Data Workflows
Production ML Systems
Observability
Responsible AI