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About the job

Company Overview

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With tens of millions of customers worldwide, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Job Overview

Intuit is looking for an innovative and hands-on Senior AI Scientist to join the Mid-Market Intelligence team.

Mid-Market is one of the fastest growing segments of the business, and we are on a mission to build the industry's first truly AI-native ERP. The Intelligence team builds the AI at the heart of that product — leveraging the state of the art in AI and agentic technology to enable AI-native experiences where intelligent automation and seamless AI-to-human collaboration are designed in from the ground up, not bolted on. Come join our collaborative and creative group of AI scientists and machine learning engineers and build models and agentic systems that directly change how mid-market businesses run their operations. In this role you will be building, evaluating, and deploying AI systems spanning large language models, agentic architectures, and classical machine learning.

Responsibilities

- Apply the state of the art in AI and agentic technology — LLMs, multi-agent architectures, tool use, retrieval (RAG), prompt and context engineering, and fine-tuning — to enable AI-native product experiences for mid-market ERP customers

- Perform hands-on data analysis and modeling with huge, real-world business datasets (financials, operations, transactions)

- Build intelligent systems that proactively capture data signals, prevent issues before they occur, automate processes, and improve customer decision making — combining generative AI with classical machine learning (supervised and unsupervised) where each is strongest

- Design and build rigorous evaluation frameworks for LLM and agentic systems — offline evals, golden datasets, LLM-as-judge approaches, and quality/safety guardrails — and treat evaluation as a first-class deliverable

- Work side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products for AI-native workflows, including human-in-the-loop and explainability patterns

- Discover data sources, get access to them, import them, clean them up, and make them 'model-ready.' You need to be willing and able to do your own ETL

- Create and refine features, context, and retrieval strategies from the underlying data. You'll enjoy developing just enough ERP subject matter expertise to have an intuition about what signals might make your models and agents perform better, and then you'll lather, rinse and repeat

- Run regular A/B tests and online experiments, gather data, perform statistical analysis, draw conclusions on the impact of your optimizations, and communicate results to peers and leaders

- Explore new AI research, design, and technology shifts in order to determine how they might connect with the customer benefits we wish to deliver, in a landscape that evolves month to month

Qualifications

- BS, MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.)

- 3+ years of industry experience with AI science, spanning both classical machine learning and Generative AI; hands-on development and production rollout of LLM-powered or agentic experiences is a huge plus

- Experience with modern AI/agentic building blocks: LLM APIs and orchestration frameworks, prompt and context engineering, RAG/retrieval and vector search, tool use and function calling, fine-tuning, and evaluation/guardrail frameworks

- 3+ years of experience in modern advanced analytical tools and programming languages such as Python (scikit-learn, PyTorch, or similar)

- Experience in data mining algorithms and statistical modeling techniques such as clustering, classification, regression, anomaly detection, recommender systems, sequential pattern discovery, and text mining

- Strong experimental rigor: experience designing A/B tests and offline/online evaluations, and drawing statistically sound conclusions

- Efficient in SQL, Hive, or SparkSQL, etc.; experience with large-scale data processing (e.g., Spark/PySpark) a plus

- Comfortable in Linux environment

- Solid communication skills: demonstrated ability to explain complex technical issues, model behavior, and evaluation results to both technical and non-technical audiences

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits ). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: Mountain View $180,000 - $243,500