Machine Learning Engineering Intern - Summer 2027

Siemens•Raleigh, NC

8h•$29 - $34•Hybrid

About The Position

We're hiring a Machine Learning Engineering Intern who is currently enrolled in a master's program and brings rigorous, logic-driven thinking. You'll work alongside our ML engineers to build, evaluate, and ship machine learning systems that help customers get more from their assets and operations. We believe some of the best ML engineers reason like philosophers, engineers, and theologians: they define terms carefully, test assumptions, and follow an argument to its conclusion. This internship is built for people who think that way and have already started writing software. This 10-week Summer Internship will last between May 2027 and August 2027.

Requirements

Current enrollment in a master's degree program for the full duration of the internship.
At least one of the following: professional software engineering experience, including full-time, part-time, contract, or substantial open-source work; a bachelor's degree in Engineering, Philosophy, Theology, Mathematics, or another logic-focused discipline; or a recognized programming certification, such as a language certification or completed coding bootcamp.
Working proficiency in Python.
Comfort with foundational mathematics for machine learning, including linear algebra, probability, and basic statistics.
The ability to explain a complex argument clearly in writing and conversation.

Nice To Haves

Hands-on experience with a machine learning framework such as PyTorch, TensorFlow, or scikit-learn.
Familiarity with Git, SQL, and working in a Linux command line.
Coursework or projects in formal logic, ethics, decision theory, or statistics.
A portfolio, GitHub profile, or writing sample that demonstrates how you think through difficult problems.
A degree background outside Computer Science at the bachelor's or master's levels.

Responsibilities

Build and maintain data pipelines that clean, label, and prepare datasets for model training.
Train, fine-tune, and evaluate machine learning models, and write clear reports on what worked and why.
Design evaluation methods and test cases that expose where a model's reasoning breaks down.
Write production-quality Python code, with tests and code review, that ships alongside our engineering team.
Help deploy and monitor models, tracking accuracy, drift, and failure cases after launch.
Present your work to the team in a final project showcase at the end of the internship.