AI Developer Intern

Search here for Career opportunities with The AZEK Company•Chicago, IL

3d•Onsite

About The Position

This internship offers a unique opportunity to work on diverse AI projects, contributing to research, analysis, and the development of AI chatbot features. Interns will gain hands-on experience in data collection, cleaning, preprocessing, and applying machine learning models to extract insights from manufacturing datasets. The role involves collaborating with cross-functional teams, communicating findings through visualizations, and learning industry best practices.

Requirements

Must be enrolled in a bachelor's program in a relevant discipline at an accredited university, with an anticipated graduation date between December 2026 and May 2028 (rising junior or rising senior).
Demonstrated strong academic performance with a minimum GPA of 3.0.
Must be available to work full-time for 12 weeks during the summer of 2026.
Must have reliable transportation to and from the work location.
Must be authorized to work in the US.

Nice To Haves

Currently pursuing a degree in Computer Science, Data Science, Engineering, or a related field.
Significant familiarity with Python, R, or similar programming languages.
Strong understanding of machine learning concepts and tools (e.g., TensorFlow, PyTorch).
Strong analytical and problem-solving skills.
Good communication skills and ability to work in a collaborative environment.

Responsibilities

Work with team members on diverse projects and tasks.
Perform research and analysis to aid project objectives.
Create reports, presentations, and necessary documentation.
Engage in team meetings and share ideas.
Learn and implement industry best practices and company standards.
Support basic development and testing of AI chatbot features, including prompts, data inputs, and user interactions.
Help troubleshoot and improve chatbot performance by assisting with debugging, evaluations, and simple integrations.
Collect, clean, and preprocess large-scale manufacturing datasets.
Apply machine learning models (e.g., regression, classification, clustering) to identify trends, anomalies, and predictive insights.
Collaborate with engineers and operations teams to translate data findings into actionable recommendations.
Develop visualizations and dashboards to communicate results clearly to technical and non-technical stakeholders.