Principal AI Scientist

Company Overview

Job Overview

Company Overview

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With over 100 million customers worldwide using our flagship products - TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. Intuit’s strategy is to be a global AI-driven expert platform delivering world-class experiences for our customers at unprecedented speed and scale. With AI at the heart of the strategy, Intuit has created a new organization led by our Chief AI Officer to drive Intuit’s evolution as a global AI technology leader through a dedicated research and innovation focus and a partnership with leading academic and industry organizations. Intuit’s AI teams have access to rich data and production grade AI platforms, enabling our scientists and engineers to move quickly from research to impact.

Job Overview

We are looking for a Principal AI Scientist to join Intuit’s AI, Research, and Futures organization and shape the future of Intuit’s product portfolio and expert tools by leveraging the latest research and technological advances in AI. You will be responsible for researching and solving complex, high-impact technical problems that unlock entirely new AI-driven experiences while also leading the development and deployment of new technologies and capabilities. Your work will directly shape how millions of Intuit’s customers, both small businesses and individuals, prosper through the effective use of AI within our platforms and products.

Responsibilities

• Define and contribute to a multi-year technical roadmap in partnership with Intuit’s AI leadership ensuring that Intuit stays on the leading edge of AI innovation through incubation and development of emerging new technologies and capabilities.

• Build end-to-end solutions that demonstrate how the state-of-the-art research in AI can drive transformative new experiences.

• Collaborate closely with business units and platform teams to accelerate testing and deployment of new technologies and capabilities into Intuit’s products and platforms.
• Ensure excellence in data quality, model evaluation, and lifecycle management through rigorous monitoring, testing, and retraining practices.
• Champion responsible AI practices, ensuring models are fair, transparent, and trustworthy.
• Mentor and grow top scientific talent, championing a culture of innovation, ethics, and scientific rigor.
• Contribute to the scientific community by presenting Intuit’s AI innovations in publications, patents, and talks.

Qualifications

• MS / Ph.D. in Computer Science, Statistics, or Applied Mathematics with focus on Artificial Intelligence / Machine Learning.
• Deep technical AI expertise especially in areas like training LLMs, reasoning models, multi-modal models and their application to one more more of these areas: Forecasting and Planning, Decision Making Under Uncertainty, Multi-objective Optimization, Learning from Human Feedback, Data Cognition, and Deep Personalization.
• Demonstrated research experience in AI through publications at leading journals and conferences is a strong plus.
• 8+ years of experience developing and deploying AI solutions that successfully solve challenging customer problems or enable radically new experiences.
• Ability to stay at the forefront of rapidly evolving AI research areas and apply new paradigms to business and product challenges.
• Experience translating business requirements into effective working prototypes coded up in languages like Python or using modeling frameworks like TensorFlow, Pytorch.
• Proven ability to influence cross-functional strategy and deliver solutions aligned with business goals.
• Outstanding communication and collaboration skills with ability to clearly articulate complex technical concepts to both technical and non-technical audiences.
• Experience with Machine Learning systems on cloud platforms like AWS, GCP, or Azure is preferred.
• Strong Computer Science / Data Science fundamentals including data structures, algorithms, performance complexity, data analysis, model training, A/B testing, and MLOps practices.