AI Engineering Intern - Fall 2026

Medpace, Inc.•Cincinnati, OH

23h•Onsite

About The Position

This internship offers a passionate individual pursuing a degree in data science and/or computer science the opportunity to work with AI tools for interactive AI applications across various IT systems at Medpace. The intern will gain experience in programming and fine-tuning AI tools, assisting IT teams in implementing them into new applications, and collaborating across the organization to improve process efficiency and provide user support.

Requirements

Pursuing a Master’s or PhD in Artificial Intelligence, Computer or Data Science, or related field
Available to work 40 hours per week in our Cincinnati, OH office
5 GPA or higher
Class or internship experience implementing and fine-tuning Large Language Models or interactive AI applications
Proficiency in programming languages and frameworks commonly used in NLP and AI (e.g., Python, TensorFlow, PyTorch)
Experience working with different AI capabilities and ability to showcase your passion for the utilization of AI

Nice To Haves

Preference for those currently in or from Cincinnati

Responsibilities

Design, implement, and deploy machine learning models and systems to solve complex problems and drive business outcomes
Research, develop, and implement machine learning algorithms and models for tasks such as classification, regression, clustering, anomaly detection, and recommendation systems
Research, develop, and implement AI tools such as NLP, LLM, and IA
Collect, preprocess, and curate large datasets required for training generative models
Experiment with different machine learning techniques and algorithms, including supervised, unsupervised, semi-supervised, reinforcement, and deep learning
Design and optimize machine learning pipelines and workflows, incorporating techniques for data cleaning, feature engineering, model selection, and hyperparameter tuning
Develop scalable and efficient machine learning infrastructure and systems for training, testing, and deploying models in production environments