Machine Learning Engineering Intern, Personal Agentic Search
Role Overview
The Personal Agentic Search team builds AI-powered agents that use personal context and data to retrieve information while preserving user privacy.
The intern will help build and evaluate machine learning systems for experiences that may reach millions of users.
The internship combines NLP, large language models, and information retrieval, with work alongside experienced engineers and researchers.
Key Skills for This Role
Full Job Posting
Team and opportunity
The Personal Agentic Search team builds AI-powered agents that use personal context and data to retrieve information while preserving user privacy.
The intern will help build and evaluate machine learning systems for experiences that may reach millions of users.
The internship combines NLP, large language models, and information retrieval, with work alongside experienced engineers and researchers.
Role responsibilities
- Help build and evaluate the machine learning systems behind personal search experiences.
- Contribute to real projects that turn research ideas into robust, user-facing products.
Minimum qualifications
- Currently pursuing a BS, MS, or PhD in Computer Science, Machine Learning, or a related field.
- Strong foundation in data structures, algorithms, and software design.
- Proficiency in at least one of Python, Swift, C++, or Java.
- Solid machine learning foundation and working knowledge of NLP fundamentals, including tokenization, embeddings, language modeling, or text classification.
- Strong communication and collaboration skills, with a desire to work in a team environment.
Preferred qualifications
- Familiarity with LLM architectures such as Transformers and attention mechanisms, and how they are trained and applied.
- Familiarity with indexing, ranking, nearest-neighbor search, or relevance evaluation.
- Experience with agentic LLM workflows such as planning, tool calling, or multi-step reasoning.
- Experience with on-device machine learning or privacy-preserving ML approaches.
- Experience designing experiments, metrics, and datasets to evaluate model or search quality.
- Proficiency in more than one of Python, Swift, and C++.
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