Machine Learning Engineering Intern, Personal Agentic Search

At Apple we build products that become part of peoples lives. Every day people rely on their devices to hold their messages documents photos and notes. The Personal Agentic Search team uses Apple Intelligence to help them find and act on that information in natural intuitive are engineers and researchers building AI-powered agents that understand a persons context reason over their personal data and retrieve exactly what they need all while preserving the privacy Apple users expect. As an intern you will help build and evaluate the machine learning systems behind these experiences and see your work shape features that millions of people could use.

This internship sits at the intersection of NLP large language models and information retrieval. You will work on real projects alongside experienced engineers and researchers helping turn research ideas into robust user-facing experiences. We are looking for someone with a strong foundation in computer science and machine learning and a real curiosity about how language models and search systems work. You dont need to be an expert in every area but you should be excited about applying ML to hard user-centered problems.

Design prototype and evaluate ML-driven approaches to personal search and retrieval using LLMs embeddings and on-device machine agentic workflows in which LLMs plan call tools and reason over multi-step search and improve NLP components such as query understanding semantic retrieval ranking and experiments analyze results and develop metrics and datasets to measure search quality and model closely with AI/ML researchers and senior engineers to turn research ideas into robust user-facing clean efficient and well-tested code in languages like Python Swift and your findings and learnings to the team.

Currently pursuing a BS MS or PhD in Computer Science Machine Learning or a related foundation in computer science fundamentals (data structures algorithms software design) and proficiency in at least one programming language such as Python Swift C or foundation in machine learning and working knowledge of NLP fundamentals (tokenization embeddings language modeling text classification) gained through coursework research personal projects or professional communication and collaboration skills with a desire to work in a team environment.

Familiarity with LLM architectures (e.g. Transformers attention mechanisms) and how they are trained and with search and retrieval algorithms such as indexing ranking nearest-neighbor search and relevance with agentic LLM workflows such as planning tool calling and multi-step with on-device machine learning or privacy-preserving ML designing experiments metrics and datasets to evaluate model or search in more than one of Python Swift and C.