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Intuit is seeking a visionary Staff AI Scientist to lead the design and development of next-generation foundational language models and agentic systems for financial intelligence. You will work within the Foresight team, which builds advanced AI technology that transforms Intuit's proprietary financial data and domain expertise into customer-facing products serving millions of small and medium-sized businesses across QuickBooks and related platforms.
In this role, you will set technical direction and strategy for agentic and foundation-model systems across the organization. You will design and develop foundational language models and agentic architectures that enable intelligent decision-making for SMBs, exploring novel approaches in large-scale pretraining, fine-tuning (SFT, RLFT, GRPO), retrieval-augmented generation, and reasoning systems. Your work will focus on improving factuality, adaptability, and alignment while solving real financial workflows—from autonomous accounting and bookkeeping to planning, communication, and insight generation.
You will leverage Intuit's unique data moat, including counterparty behavior signals, two-sided transaction visibility, and business twins, to create defensible modeling advantages. A core responsibility is shipping end-to-end solutions from experimentation to production, owning evaluation frameworks and metrics that demonstrate real-world impact. You will mentor scientists and engineers, review technical designs, and elevate the craft of the wider team—a core expectation at the Staff level.
You will collaborate closely with AI research, engineering, product, and design teams to translate breakthrough science into scalable products. Publishing research findings, contributing to open-source frameworks, and sharing knowledge across the organization are expected.
Required qualifications include a Ph.D. or Master's degree in Computer Science, Math, Machine Learning, AI, or related field with a track record of technical leadership on complex AI systems. You must have proven experience with LLMs, multimodal models, or foundation models (pretraining, fine-tuning, deployment), deep understanding of transformer architectures, reinforcement learning, reasoning models, and agentic systems, and strong Python proficiency with modern ML and agentic frameworks. Demonstrated ability to build end-to-end AI pipelines at scale, influence technical direction across multiple teams, and mentor other scientists and engineers is essential.
Nice-to-have qualifications include experience with RL fine-tuning (GRPO, RLHF/RLFT), background in financial/accounting/business-domain AI or probabilistic forecasting, and publications at top ML venues or meaningful open-source contributions.
