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Procter & Gamble · 1 day ago

R&D PhD Summer Intern – Machine Learning / AI

Mason, OH

Internship

Onsite

Intern

$60.57/hr - $60.57/hr

Start in 2026 Spring

Procter & Gamble is the largest consumer packaged goods company in the world, and they are seeking a PhD intern for their Corporate R&D to develop machine learning models. The role involves exploring literature, developing data pipelines, and sharing insights to improve time-series data modeling.

BeautyBrand MarketingCleaning ProductsConsumer GoodsCosmeticsNutritionPersonal Health

Growth Opportunities

No H1B

Responsibilities

Explore existing literature and techniques in self-supervised and semi-supervised learning to devise a strategy for using large unlabeled datasets to improve time-series data modeling

Develop a functional pipeline that demonstrates improved performance in time-series data modeling (e.g., classification) using available unlabeled datasets

Design and implement robust and efficient data pipelines to effectively manage and handle large volumes of data for machine learning training tasks

Share key insights from your research and development process with the broader team to improve our collective understanding of self-supervised and semi-supervised learning strategies

Qualification

Machine LearningDeep LearningPyTorchTensorFlowTime Series ModelingMulti-GPU TrainingSelf-Supervised LearningSemi-Supervised LearningData ManagementSQLPandasSparkGit/GitHubCI/CD ToolsDocumentationCommunication SkillsProblem-SolvingAdaptability

Required

Education: Working towards a PhD in Computer Science, Computer Engineering, Data Science, or related fields

Available to work a 12-week internship from May/early June to August in the summer of 2026. Available to work 5 days a week in office at Mason, OH location

Experience in developing and deploying machine learning and deep learning models

Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow

Preferred

Experience in time series data modeling

Experience in multi-GPU training

Demonstrated a proven track record of significant contributions through grants, fellowships, patents, or publications in leading machine learning workshops, journals, or conferences

Experience in self-supervised or semi-supervised learning methods

Familiarity with managing large datasets. Proficiency in data management tools (e.g., SQL, Pandas, Spark)

Proficiency in Git/GitHub for version control, familiarity with CI/CD tools for automated deployment, and ability to create and maintain clear documentation