Machine Learning Engineer, Apple Search & Knowledge Platforms at Apple

Department: Machine Learning and AI

Compensation · Listed in posting

$142,300 – $263,300

Location: Seattle, Washington
Level: mid
Posted: Sep 16, 2026

Description

The Apple Knowledge Quality Team is hiring a Machine Learning Engineer to design and develop features for large-scale knowledge question answering, including systems involving graph data, web documents, machine learning, and deep learning. The role involves building and maintaining production ML/AI systems, evaluating user experience, partnering with cross-functional teams, and supporting model deployment and monitoring. Candidates need a relevant degree or PhD, programming proficiency, experience with large-scale data systems and ML pipelines, and hands-on experience with frameworks such as PyTorch or TensorFlow.

Required Skills

Python
Scala
Go
Knowledge graphs
PyTorch
TensorFlow
Natural language processing
Model evaluation
CI/CD pipelines
Model deployment
Monitoring solutions

Additional Information

Minimum Education: Degree in Computer Science, Machine Learning, or a related field, or a PhD in a related field
Required Previous Experiences: At least 2 years of industry experience building production ML/AI systems
Experience building and maintaining large-scale data systems, knowledge graphs, and end-to-end ML pipelines in production
Hands-on experience with PyTorch or TensorFlow in production environments
Experience with natural language processing, statistical data analysis, and model evaluation methodologies
Experience collaborating with cross-functional product, engineering, and data science teams
Experience with CI/CD pipelines, model deployment, and monitoring solutions
Preferred Qualifications: MS degree with 6+ years of industry experience building and scaling ML/AI systems, or a PhD with 3+ years of industry experience in production ML environments
Experience designing, deploying, and maintaining large-scale distributed ML systems serving millions of queries per second
Experience with A/B testing, experimentation frameworks, and data-driven product iteration at scale
Experience designing human-in-the-loop evaluation pipelines and using user feedback to improve model performance
Experience with LLM deployment, prompt engineering, fine-tuning, RAG, or other generative AI technologies in production
Experience building model monitoring, observability, and quality assurance systems for production ML services
Experience optimizing ML systems for latency, throughput, and cost at scale
Track record of shipping ML-powered features that improved user experience for consumer-facing products
Strong product intuition and ability to translate business requirements into technical solutions

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