R&D PhD Summer Intern: AI Agents for Automated Machine Learning Research

Procter & Gamble

9d•Onsite

About The Position

Research and Development (R&D) at Procter & Gamble, the largest consumer packaged goods company in the world, includes a diverse group of roles that contribute to the innovation and development of our products. It encompasses roles in product research, formulation, testing, and scientific analysis. You will find variety and excitement starting Day 1. This internship is specifically designed for individuals working towards a PhD who are developing proficiency in their field. As an intern or co-op in management, you will have the opportunity to learn from experienced professionals in a supportive environment. This is a 12-week paid internship, designed to provide you with a solid foundation for future career growth. The internship will take place from May (potentially early June) to August of 2027. Join us at P&G, where your contributions will play a vital role in shaping the future of consumer products! The Opportunity: P&G has an opportunity for a PhD intern to work in our Corporate R&D to develop an AI-agent-driven framework that automates and accelerates machine learning experimentation for time-series data modeling. The intern will explore how AI agents can propose and execute experiments, modify configurations or code, analyze results, and recommend next steps. This work will investigate improvements in data preprocessing, model architecture, training strategies, and hyperparameter selection while comparing AI-driven research with conventional experimentation in terms of model performance, reproducibility, and research efficiency. The ideal candidate will demonstrate a strong eagerness to learn and grow professionally and possess excellent communication skills—both written and verbal. This role is perfect for those with passion for innovation and problem-solving, along with a proactive attitude and the ability to adapt to new challenges. Join us in this dynamic environment, where your contributions will make a real impact as part of a collaborative team!

Requirements

Working towards a PhD in Computer Science, Computer Engineering, Data Science, Electrical Engineering, Statistics, or a related field.
Experience developing and evaluating machine learning or deep learning models.
Hands-on experience with a deep learning framework such as PyTorch or TensorFlow.
Proficiency in Python.
Understanding of model training, validation, performance evaluation, and experimental design.
Ability to review technical literature, formulate research hypotheses, and translate ideas into reproducible experiments.
Available to work a 12-week internship from May/early June to August in the summer of 2027.
Available to work 5 days a week in office at Mason, OH location.

Nice To Haves

Experience with large language models, generative AI, and the design of multi-step agentic workflows or multi-agent systems.
Experience with agentic frameworks such as LangGraph and AI observability platforms such as Arize.
Experience with automated machine learning, hyperparameter optimization, experiment orchestration, or experiment-tracking systems.
Experience in time-series data modeling.
A record of research contributions through grants, fellowships, patents, peer-reviewed publications, conference presentations, or relevant open-source projects.

Responsibilities

Review existing literature and techniques in LLM-based AI agents, automated machine learning, and AI-enabled research systems.
Prepare time-series datasets and establish a reliable, reproducible baseline model and evaluation pipeline.
Design and implement an AI-agent-driven framework for proposing, executing, and analyzing machine learning experiments.
Explore improvements in data preprocessing, feature representation, model architecture, training strategies, and hyperparameter selection.
Implement appropriate validation, experiment-tracking, and reproducibility practices for AI-generated configurations or code.
Compare AI-enabled and conventional experimentation approaches based on model performance, reproducibility, experiment throughput, and research efficiency.
Document the framework, experiments, results, limitations, and recommendations.
Share key insights and present final results to the broader R&D team.