Apple
Machine Learning Engineer, Apple Search & Knowledge Platforms
$142K–$263K
Seattle, Washington · Full-time · Posted Sep 16, 2026
- Experience
- 2+ years of industry experience building production ML/AI systems, or a PhD in a related field. Preferred: MS degree with 6+ years of industry experience building and scaling ML/AI systems, or PhD with 3+ years of industry experience in production ML environments.
- Education
- Degree in Computer Science, Machine Learning, or related field, or a PhD in a related field.
About the job
Build and evaluate production machine-learning systems for Knowledge Q&A that power features such as Siri and Spotlight.
What you'll do
- Design and develop platform features involving large-scale data management, machine learning, and deep-learning systems over graph data and web documents.
- Measure, evaluate, and analyze user experience to inform product evolution.
- Partner with cross-functional teams to deliver search results that meet users’ information needs.
What they ask for
- Degree in Computer Science, Machine Learning, or a related field plus 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 production end-to-end ML pipelines; hands-on production experience with PyTorch or TensorFlow.
- Experience with natural language processing, statistical data analysis, model evaluation, cross-functional collaboration, and CI/CD, deployment, and monitoring.
Nice to have
- Experience with large-scale distributed ML systems, A/B testing, human-in-the-loop evaluation, or production generative AI technologies.
- Experience optimizing production ML systems and track record shipping ML-powered consumer features that improve user experience.
Pay
$142,300 and $263,300 (base salary)
Tools
Python, Scala, Go, Apache, Spark, PyTorch, TensorFlow, CI/CD
AI focus
Production machine learning and deep learning for Knowledge Q&A, including preferred work with LLM deployment, prompt engineering, fine-tuning, RAG, and other generative AI technologies.
Model-written summary · the original posting is authoritative