Machine Learning Engineer Intern

Workday•Pleasanton, CA

29d•Onsite

About The Position

Workday is seeking a Machine Learning Engineer Intern to contribute to the development of Workday’s intelligent core. This role involves working on scalable AI and ML platforms that power personalized experiences for millions of users. Interns will collaborate with experienced engineers and researchers to design, implement, and deploy machine learning models and large language model applications. The internship focuses on writing production-quality code, running experiments, and working with large datasets to enhance model and system performance, offering a chance to apply software engineering and machine learning knowledge to real-world products at scale.

Requirements

Currently enrolled in a Bachelor’s or Master's degree program in Computer Science, Machine Learning, Data Science, Artificial Intelligence, Software Engineering, Computer Engineering, Mathematics, Statistics, or a related degree program
Less than 12 months of previous professional work experience
Anticipated graduation date between December 2027 and June 2028
Available for a full-time, in-person, 12-week internship for the Summer of 2027
Experience with both software engineering and machine learning engineering concepts through coursework, research, internships, or personal projects
Experience programming in Python or another language used for machine learning, data processing, or software development
Experience with one or more of the following: Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), Pandas, PySpark, or AWS
Familiarity with AI tooling such as Gemini, Claude, Cursor, Codex, or similar tools

Nice To Haves

Interest in LLM-based approaches, such as retrieval, prompt tuning, embeddings, or model evaluation
Curiosity and eagerness to learn, with an interest in how AI and ML systems are developed, tested, deployed, and improved over time
Excellent communication skills, both verbal and written, with the ability to share technical ideas clearly and collaborate across teams

Responsibilities

Contribute across the full machine learning lifecycle, from data processing and experimentation through model and system deployment
Apply machine learning and AI techniques, including deep learning and LLM-based approaches such as retrieval and prompt tuning, to power search and enhance the user experience
Write production-quality code and build scalable components that support AI and ML products across the Workday platform
Build and deploy APIs and microservices using a modern engineering stack, including Docker and Kubernetes
Design and run evaluations to measure model and system quality, using results to guide what to improve or build next