Job description

Intuit’s Virtual Expert Platform (VEP) connects millions of customers with the right expert, guidance, and action at the right moment across TurboTax, QuickBooks, and Credit Karma. We are looking for a Senior AI Scientist to be the technical architect behind how we recommend, rank, and personalize those connections. In this role you will move beyond simple click-prediction to build a holistic understanding of the customer journey—predicting not just what a customer needs, but when they need it across multiple product verticals. You will own recommendation and ranking models end-to-end: from data discovery and feature engineering through production deployment, A/B experimentation, and ongoing optimization. Your work will directly shape which experts customers see, which products surface at the right time, and how we balance immediate business value with long-term customer trust and lifetime value. This is a highly cross-functional role. You will partner closely with product managers, software engineers, designers, and analytics teams to define metrics, translate model performance into business outcomes, and ship solutions that work at scale. We value scientists who act like owners: identifying problems, proposing solutions, and seeing them through to launch. Responsibilities • Design, build, and deploy large-scale recommendation and ranking models that predict what a customer needs and when they need it across multiple product verticals. • Architect multi-stage recommendation systems—including candidate retrieval, scoring, re-ranking, and blending—that serve personalized results in real time. • Build recommendation logic that balances immediate revenue with long-term customer trust and lifetime value, managing trade-offs across competing product categories. • Perform hands-on data analysis and modeling with massive datasets. Discover data sources, build ETL pipelines, clean and import data, and make it model-ready. • Engineer and iterate on features from underlying data, combining domain understanding with statistical rigor to continuously improve model performance. • Design and run A/B tests, perform rigorous statistical analysis, draw causal conclusions, and communicate actionable insights to peers and leadership. • Collaborate closely with partner teams to define metrics that quantify revenue, engagement, customer experience, and long-term member value. • Represent AI Science in cross-functional meetings and reviews. Translate complex technical concepts into clear narratives for business stakeholders. • Explore emerging techniques—including generative AI, reinforcement learning, and agentic architectures—and evaluate how they can improve recommendation quality or enable new customer experiences. Qualifications Required • MS or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, Physics, or a related quantitative discipline. • 4+ years of industry experience building and deploying machine learning models in a production environment (consumer tech, fintech, or e-commerce preferred). • Expert proficiency in Python and SQL, with deep experience in modern ML/DL frameworks (PyTorch, TensorFlow, scikit-learn, pandas, NumPy). • Strong experience with ranking, retrieval, and multi-stage recommendation architectures. • Solid foundation in statistical modeling, and ML techniques: classification, regression, clustering, neural networks, decision trees, anomaly detection, and recommender systems. • Proficiency working with large-scale data ecosystems (Hive, Spark, SparkSQL, or equivalent). • Comfortable working in a Linux environment. • Demonstrated ability to explain complex technical concepts and trade-offs to both technical and non-technical audiences, and to link model performance to business outcomes. Preferred • Deep learning expertise applied to recommendation, personalization, or representation learning (embeddings, two-tower models, sequence models, transformers). • Experience with NLP/NLU techniques—such as text representation, semantic similarity, and transformer models—in the context of improving recommendations or customer matching. • Familiarity with agentic AI patterns: tool calling, multi-step reasoning, or orchestrating LLM-based workflows to augment recommendation or decision-support systems. • Exposure to reinforcement learning or dynamic optimization for real-time decision-making systems. • Expertise in computer science fundamentals: data structures, algorithms, and performance considerations. • Experience in a marketplace, ads, or matching environment where you manage trade-offs between competing objectives. 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: The expected base pay range for this position is: Mountain View $180,000 - $243,500

Requirements

Must-haves

Design, build, and deploy large-scale recommendation and ranking models that predict what a customer needs and when they need it across multiple product verticals.
Architect multi-stage recommendation systems—including candidate retrieval, scoring, re-ranking, and blending—that serve personalized results in real time.
Build recommendation logic that balances immediate revenue with long-term customer trust and lifetime value, managing trade-offs across competing product categories.
Perform hands-on data analysis and modeling with massive datasets. Discover data sources, build ETL pipelines, clean and import data, and make it model-ready.
Engineer and iterate on features from underlying data, combining domain understanding with statistical rigor to continuously improve model performance.
Design and run A/B tests, perform rigorous statistical analysis, draw causal conclusions, and communicate actionable insights to peers and leadership.
Collaborate closely with partner teams to define metrics that quantify revenue, engagement, customer experience, and long-term member value.
Represent AI Science in cross-functional meetings and reviews. Translate complex technical concepts into clear narratives for business stakeholders.
Explore emerging techniques—including generative AI, reinforcement learning, and agentic architectures—and evaluate how they can improve recommendation quality or enable new customer experiences.
MS or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, Physics, or a related quantitative discipline.
Expert proficiency in Python and SQL, with deep experience in modern ML/DL frameworks (PyTorch, TensorFlow, scikit-learn, pandas, NumPy).
Strong experience with ranking, retrieval, and multi-stage recommendation architectures.
Solid foundation in statistical modeling, and ML techniques: classification, regression, clustering, neural networks, decision trees, anomaly detection, and recommender systems.
Proficiency working with large-scale data ecosystems (Hive, Spark, SparkSQL, or equivalent).
Comfortable working in a Linux environment.
Demonstrated ability to explain complex technical concepts and trade-offs to both technical and non-technical audiences, and to link model performance to business outcomes.

Nice-to-haves

4+ years of industry experience building and deploying machine learning models in a production environment (consumer tech, fintech, or e-commerce preferred).
Deep learning expertise applied to recommendation, personalization, or representation learning (embeddings, two-tower models, sequence models, transformers).
Experience with NLP/NLU techniques—such as text representation, semantic similarity, and transformer models—in the context of improving recommendations or customer matching.
Familiarity with agentic AI patterns: tool calling, multi-step reasoning, or orchestrating LLM-based workflows to augment recommendation or decision-support systems.
Exposure to reinforcement learning or dynamic optimization for real-time decision-making systems.
Expertise in computer science fundamentals: data structures, algorithms, and performance considerations.
Experience in a marketplace, ads, or matching environment where you manage trade-offs between competing objectives.
Mountain View $180,000 - $243,500

Application prep

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Keywords to mirror

aimachine learningpythonsqlmlllm

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