Intern, AI Science

Company Overview

Job Overview

Come join the FILM (Foundational Intuit LLMs) group as an Intern, AI Scientist. We are

building the Intuit Foundational LLM, as part of a proprietary Generative AI operating system

(GenOS) platform.

Responsibilities

How you’ll contribute:

As an AI Scientist Intern, you will have the opportunity to apply and grow your skills in

natural language processing (NLP) and large language models (LLMs). You will work

with diverse, multi-modal data types at a massive scale, leveraging proprietary Intuit

data to help unlock insights. This role will provide you with hands-on experience in

advanced model fine-tuning, transfer learning, prompt engineering, few-shot

learning, and data augmentation methods to build both predictive and generative

models, fueling innovation across Intuit products.

You will collaborate with our cross-functional teams—including data engineers, ML

architects, product managers, and business analysts—to support the development of

high-performance LLM pipelines. You will assist in designing and executing research

strategies for optimizing model architecture, prompt optimization, tokenizer

customization, data curation, noise reduction, and hyper-parameter tuning to meet

Intuit’s complex and large-scale data challenges.

Under the guidance of senior team members, you will help provide stakeholders with

an understanding of how to utilize LLM models, embeddings, and vector databases

to meet critical business needs. You will also have the chance to contribute to projects

that are at the forefront of advancements in generative AI, reinforcement learning,

and self-supervised learning.

This internship will immerse you in the end-to-end development of LLM workflows,

from hypothesis generation and model fine-tuning to data preprocessing and A/B

testing. You will be part of a continuous feedback loop for model retraining and

precision tuning, ensuring alignment with shifting data scales and complex

multi-domain applications.

You will also learn to use interpretability tools to understand LLM outputs and

contribute to a team that is focused on maximizing the impact of cutting-edge AI

capabilities.

Qualifications

What it takes

• Strong NLP and LLM Knowledge: A foundational understanding of NLP techniques and a keen interest in LLM technologies. Coursework or projects in this area are highly desirable.

• Passion for Emerging AI Technologies: A demonstrated interest in the

  advancements in NLP , LLMs, generative AI, machine learning, and deep learning. A

  desire to stay current with the latest developments in transformer architectures,

  self-supervised learning, and model fine-tuning is essential.

• Robust Technical Expertise in Data Science and LLMs: A solid understanding of the

  data science principles that underlie LLMs, including tokenization, embeddings,

  pre-training and fine-tuning methods, data augmentation, and prompt engineering.

• Global Collaboration: The ability to collaborate effectively with cross-functional

  teams and partners in a global setting to contribute to complex, LLM-focused projects.

• Adaptability and Eagerness to Learn: A quick learner with the flexibility to thrive in a

  fast-paced, innovation-driven environment and adapt to evolving LLM techniques and

  tools.

• Exceptional Communication Skills: Strong verbal and written communication skills,

  with the ability to participate in discussions and explain AI concepts to both technical

  and non-technical audiences in a clear manner.

• Project and Stakeholder Engagement: An interest in learning how to manage

  complex projects, align with multiple stakeholders, and contribute to data-driven

  initiatives.

Advantages

• Familiarity with end-to-end AI projects (from inception to production). We primarily

  use Python in all stages of development.

• Comfortable working in a Linux environment.

• Exposure to building end-to-end reusable pipelines, from data acquisition to model

  output delivery.