Sr Staff AI Scientist, Agentic AI & Recommendations

Technical Leader For Agentic Ai And In-Product Recommendation Systems

Intuit's Consumer Group (CG) AI is in the business of creating impactful, intelligent experiences that save time and money for our customers. Our work spans two deeply connected fronts: agent-powered experiences that reason, plan, and act on complex user challenges, and the in-product recommendation and personalization systems that surface the right guidance, products, and next-best-actions to millions of customers at exactly the right moment.

We are looking for a technical leader who is equally at home architecting agent systems and building production recommendation systems grounded in strong classical ML foundations — ranking, retrieval, candidate generation, uplift modeling, and bandits — to drive measurable customer and business outcomes.

If you like designing and deploying ML systems that meaningfully change what customers see and do inside a product — and want to push the frontier on agentic AI at the same time — come, join us.

In this high-impact role, you will be a key technical leader in architecting and delivering both next-generation Agentic AI solutions and the in-product recommendation systems that ensure our customers experience more money, less work, and complete confidence.

Responsibilities

• Drive the initiation and design of complex recommendation and agent model solutions. Lead the end-to-end architecture and implementation of the team's work, ensuring accountability for high-quality code, robust design, cost efficiency, and implementation standards.
• Design, build, and deploy in-product recommendation and personalization systems at scale — including candidate generation, learning-to-rank, retrieval, and ranking architectures — that directly drive customer engagement and business metrics.
• Apply both classical and cutting-edge techniques — including recommendation and ranking systems, gradient-boosted trees, collaborative filtering, embeddings, Causal-ML and uplift modeling, multi-armed bandits, Reinforcement Learning, Online Learning, and Deep Learning — to design and train robust, self-improving systems on large, real-world datasets.
• Practice strong leadership and communication skills to influence teams and evangelize the impact of recommendation systems and Agentic AI across the broader organization.
• Partner closely with Product Managers, Software Engineers, and Designers to define problem statements, success criteria, align model metrics with core business goals, and ensure successful delivery and integration of complex ML solutions into the product.
• Work independently and proactively in a fast-paced environment. Quickly research, explore, and enable new ML, recommendation, and Agentic technologies, staying current with developments in academia and industry to solve Intuit customer problems.
• Develop efficient techniques for designing, evaluating, and continuously improving recommendation and agentic systems through offline and online (A/B, interleaving, bandit) experimentation.

Qualifications

• 8+ years of industry experience with AI science and machine learning.
• BS, MS, or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent.
• 7+ years of hands-on expertise across core and advanced ML paradigms, including recommendation systems, ranking/retrieval, and classical ML (e.g., gradient boosting, logistic regression, collaborative filtering, feature engineering), as well as Causal-ML, Reinforcement Learning, Online Learning, and Deep Learning.
• Proven experience building and shipping in-product recommendation or personalization systems in production, with demonstrable customer or business impact.
• Have extensive prior experience building end-to-end, reusable data and model pipelines — from data acquisition through to complex model/agent output delivery — in a production environment.
• Strong business acumen to understand end-to-end impact.

• Experience designing large-scale recommendation systems with two-stage (candidate generation + ranking) architectures and online experimentation
• Experience with multi-armed bandits, contextual bandits, or uplift modeling for in-product decisioning
• Experience developing and evaluating complex agentic AI systems
• Experience orchestrating multi-agent systems
• Authored papers in top conferences and journals on Recommendation Systems, Reinforcement Learning, or Deep Learning