AIML - Machine Learning Researcher, Data and ML Innovation

Apple
Seattle, United States of America2025-07-24

About the job

The AIML Information Intelligence team is creating groundbreaking technology for artificial intelligence, machine learning and natural language processing! The features we create are redefining how hundreds of millions of people use their computers and mobile devices to search and find what they are looking for. Our universal search engine powers search features across a variety of Apple products, including Siri, Spotlight, Safari, Messages and Lookup. We also develop innovative generative AI technologies based on Large Language Model to power innovative features in both Apple’s devices and services on the cloud.

Responsibilities

Analyzing search ranking and relevance requirements, issues and opportunities

Developing, fine-tuning, and evaluating domain specific Large Language Models for various tasks and applications in Apple’s AI powered products

Conducting applied research to transfer the pioneering research in generative AI to production ready technologies

Understanding product requirements, translate them into modeling tasks and engineering tasks

Building machine learned models for search relevance, ranking and query understanding problems

Integrating search functions into Apple products, such as Siri, Spotlight, Safari, Messages, Lookup, etc.

Building end-to-end production system including query understanding and ranking to power search

Applying Spark, Hadoop MapReduce, Hive, Impala to perform distributed data processing

Qualifications

Minimum

PhD, MS or equivalent experience

Experience in machine learning, deep learning, information retrieval, natural language processing or data mining

Experience of prompt engineering, fine-tuning, evaluating, and developing data collection/annotation/management tooling for LLMs.

Proficiency in one of following languages: Python, Go, Java, C++

Preferred

Excellent data analytical skills

Good interpersonal skills and team player

PhD preferred