AI/ML Engineering Intern - 218174

Key Skills

Aartificial intelligence, C++, Deep Learning, Jupyter Notebook, Large Language Models - LLMs, Machine learning techniques, Python Programming, SQL

Job Description

What You’ll Do

Teradata, the world's leading data analytics company is seeking a motivated and enthusiastic AI/ML Engineering Intern to join our team. In this role you will be working with the Innovation team to run AI and Machine Learning experiments in the Generative AI space to analyze the capability and application of multiple types of Large Language Models (LLMs).

• Research and identify off-the-shelf, open-source or self-hosted LLMs for testing or fine-tuning
• Construct a testing ML pipeline for measuring and capturing model performance metrics
• Develop an understanding of the cost-benefit equation for different modalities of LLM deployments
• With support from senior researchers, share your findings within the broader organization to help direct technical strategy
• The successful candidate will be able to make recommendations for models based on different use cases

Who You’ll Work With:

The Technology and Innovation Office is dedicated to solving broader information integration problems through new and unique methods. We work across organizations to gather input from customers and stakeholders to build technology prototypes, develop new ideas and evangelize state-of-the-art analytics. You will be reporting to the Data Science team responsible for Generative AI initiatives.

What Makes You a Qualified Candidate

• Language skills - Python, SQL and some familiarity with C++
• Experience with machine learning libraries and deep learning frameworks (scikit-learn, mlflow, tensorflow/pytorch)
• Experience using public CSP
• With graduation dates between December 2024-June 2026.

What You’ll Bring

In addition to the basic requirements, the ideal candidate would possess excellent verbal communication and technical writing skills in order to communicate with a broad audience.

• Experience with Jupyter Notebooks
• Prior familiarity with fine-tuning LLMs or other foundation models
• A knack for exploratory experimentation, finding what works and what breaks state-of-the-art technologies