About the job
As an AI Scientist Intern at Intuit, you won't just be observing; you'll be on the front lines. You'll work side-by-side with our world-class AI Scientists, Researchers and Machine Learning Engineers, collaborating with Data Analysts, Software Engineers, and Product Managers. This is your chance to uncover critical insights and develop powerful machine learning models that directly understand and enhance customer experiences across Intuit's products.
Responsibilities
Gather, clean, and explore large, real-world datasets to uncover patterns and prepare them for modeling, including your own data wrangling and ETL.
Build, train, and evaluate machine learning and deep learning models using techniques such as classification, regression, clustering, and neural networks.
Engineer and refine features from raw data, developing intuition for what drives model performance.
Design and run experiments (e.g., A/B tests), perform statistical analysis, and communicate the impact of your models to peers and leaders.
Dive into current AI/ML research and methodologies, including generative AI and LLMs, to inform new solutions and stay ahead of the curve.
Partner with AI Scientists, Machine Learning Engineers, Data Analysts, Software Engineers, and Product Managers to translate insights into product impact.
Develop proofs-of-concept for innovative, AI-driven features or solutions.
Create clear documentation of your research, model architectures, code, and experimental results for both technical and non-technical audiences.
Qualifications
Minimum
Currently enrolled in a PhD program in Computer Science or a related technical field. Alternatively, a Bachelor's or Master's program with prior relevant experience and a graduation date after the internship ends.
Must be legally authorized to work in the United States on a full-time basis for the duration of the internship.
Ability to work in a hybrid environment at an Intuit office location determined by your team and role (relocation stipend provided).
Familiarity with core machine learning techniques (regression, classification, clustering, optimization) and their mathematical foundations.
Ability to explore, discover, and import data from multiple sources and make it machine-learning ready.
Strong programming skills in Python; SQL proficiency.
Curiosity about generative AI and large language models, and interest in applying AI tools to accelerate your own work.
Excellent communication skills and a learning mindset.
Preferred
No preferred qualifications listed.