AI Engineer Intern - Data, ML & Growth Impact (Hybrid)

Pattern; Lehi (UT); Hybrid; USD 28,000 - 48,000; Full time

Job summary

Pattern is seeking an AI Engineer Intern at our Lehi, Utah headquarters for Summer 2027. You will work on data preprocessing, model development, and deployment, tackling real-world ecommerce AI problems with guidance from senior engineers. This 12-week internship offers hands-on impact and collaboration with a seasoned AI team. You should be pursuing a CS/Engineering/Data Science degree and know Python or Java, with ML framework familiarity.

Qualifications

• Pursuing a Bachelor's or Master's in CS, Engineering, Data Science, or a related field.
• Foundational knowledge of machine learning concepts and algorithms.
• Proficiency in Python or Java.
• Familiarity with TensorFlow or PyTorch preferred.
• Strong problem-solving, analytical, and communication skills.

Responsibilities

• Assist with data preprocessing, cleaning, and analysis to prepare high-quality datasets for model training.
• Support the development and optimization of machine learning models, including hyperparameter tuning and evaluation.
• Assist in deploying AI solutions by collaborating with senior engineers to integrate models into production systems.
• Document AI experiments, code, and project workflows for reproducibility.
• Research cutting-edge AI technologies to contribute to team innovation.

Skills

Python

Java

ML concepts

Communication

Problem solving

Education

Bachelor's or Master's in Computer Science / Engineering / Data Science

Tools

TensorFlow

PyTorch

Job description

Pattern is seeking an AI Engineer Intern at our Lehi, Utah headquarters for Summer 2027. You will work on data preprocessing, model development, and deployment, tackling real-world ecommerce AI problems with guidance from senior engineers. This 12-week internship offers hands-on impact and collaboration with a seasoned AI team. You should be pursuing a CS/Engineering/Data Science degree and know Python or Java, with ML framework familiarity.