This role pays less than
77%
of similar roles. Most pay
$208,575–$254,750
— the shaded band above.
At the midpoint, this role pays about
$203k
versus about
$232k
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
3513 open roles
on FindRole.
Listed pay typically runs
$165,800–$277,600
across 2730 roles with salary data.
Machine Learning Engineer, Apple Search & Knowledge Platforms joins the Knowledge Quality team to develop next-generation machine learning solutions for Knowledge Q&A powering Siri and Spotlight. This role involves designing and developing features for a platform involving large-scale data management, deep learning systems over graph data, and web documents. The engineer will build end-to-end ML pipelines, manage knowledge graphs, and perform measurement, evaluation, and analysis of the user experience to improve information seeking results. Required technical skills include proficiency in Python, Scala, and Go, along with experience using PyTorch or TensorFlow. Candidates should possess expertise in natural language processing, statistical data analysis, and model evaluation. Additional preferred competencies include LLM deployment, prompt engineering, RAG, and optimizing systems for latency and throughput while managing high-volume production environments involving millions of queries per second.
What does a Machine Learning Engineer earn in Washington?
Median $241750 from 63 postings across 12 companies.
Design and develop machine learning systems for Knowledge Question Answering in Siri and Spotlight.
Build and maintain large-scale data management systems and knowledge graphs using the Apache software stack.
Develop end-to-end ML pipelines for processing web documents and graph data at scale.
Implement and optimize production models for high-throughput environments with millions of queries per second.
Deploy and manage generative AI technologies including LLMs, fine-tuning, and Retrieval-Augmented Generation (RAG).
Create model monitoring, observability, and quality assurance systems for production ML services.
Conduct A/B testing and human-in-the-loop evaluation to improve model performance based on user feedback.
Optimize machine learning systems for latency, throughput, and cost efficiency in high-performance computing environments.
What we're looking for
A degree in Computer Science, Machine Learning, or a related field with 2+ years of industry experience building production ML/AI systems, or a PhD in a related field.
Proficiency in mainstream programming languages such as Python, Scala, and Go.
Experience building and maintaining large-scale data systems, knowledge graphs, and end-to-end ML pipelines in production, ideally using the Apache software stack.
Hands-on experience with machine learning frameworks like PyTorch or TensorFlow in production environments.
Experience with natural language processing, statistical data analysis, and model evaluation methodologies.
Demonstrated ability to collaborate with cross-functional teams including product, engineering, and data science.
Experience with CI/CD pipelines, model deployment, and monitoring solutions.
Python
Scala
Go
PyTorch
TensorFlow
NLP
Large Language Models
Spark
RAG
Transformer Architecture
Machine Learning Systems
Graph Data
Information Retrieval
Python
Scala
Go
PyTorch
TensorFlow
NLP
Large Language Models
Spark
RAG
Transformer Architecture
Machine Learning Systems
Graph Data
Information Retrieval
LLM
PyTorch
TensorFlow
JAX
RAG
Natural Language Processing
Information Retrieval
Distributed Systems
Inference Optimization
Machine Learning
Deep Learning
LLM
Machine Learning
PyTorch
TensorFlow
JAX
Natural Language Processing
RAG
Information Retrieval
Distributed Systems
Inference Optimization
Deep Learning
Software Engineering
Generative AI
Large Language Models
Transformers
RAG
PyTorch
TensorFlow
Python
Go
Scala
Apache Spark
Kubernetes
gRPC
Information Retrieval
Ranking
Query Understanding
Data Pipelines