Senior Applied Scientist, Agentic AI

About the role

The Agentic AI team at Zillow is transforming the real estate industry by helping millions of people navigate one of the most important decisions of their lives with AI powered guidance. Our mission is to redefine the home shopping and transaction experience through an always-on assistant that combines deep real estate expertise, grounded data access, and advanced reasoning. This lean, cross-functional team of applied scientists and engineers delivers production-grade AI systems through strong collaboration, accountability, and technical rigor. Within the Agentic AI org, the Applied Reasoning team advances the reasoning depth, quality, and domain intelligence of Zillow's AI agents. We build high-value agentic capabilities that require multi-step reasoning, structured synthesis, tool grounded execution, and rigorous quality evaluation to operate reliably in live customer experiences. Our goal is to enable Zillow's AI to reason, plan, and act with the depth and judgment of top-performing real estate professionals. Zillow is looking for a Senior Applied Scientist on the Applied Reasoning team within the Agentic AI organization to research and develop advanced LLM-powered reasoning systems specialized in the real estate domain. In this role, you'll take a hands-on approach to prototyping, evaluating, and deploying agents capable of deep, multi-step reasoning, accurate use of tools/skills, and contextual decision-making tailored to real estate use cases. Your work will focus on eliciting deep reasoning and strong information synthesis from LLMs through test-time scaling and targeted post-training, supported by rigorous evaluation frameworks to improve the reliability and quality of agent responses in real estate use cases. As a Senior Applied Scientist in the Agentic AI team, you will: Design, prototype, and build advanced agentic systems capable of highly autonomous, context-aware, and adaptive interactions across diverse real estate use cases Apply test-time scaling and post-training techniques to develop agents that can reason, collaborate, compete, or negotiate in dynamic, goal-driven environments to fulfill user needs. Define and refine evaluation and experimentation processes for LLM-driven applications. Stay at the forefront of agentic AI research and innovation, bringing emerging techniques into practical application to shape product direction. Contribute to the broader scientific community through publications, conference presentations, and internal knowledge sharing Mentor and guide junior scientists and engineers, promoting best practices in applied research, scalable agentic architecture, and responsible AI development. This role has been categorized as a Remote position. "Remote" employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice, which must be identified to the Company. U.S. employees may live in any of the 50 United States, with limited exceptions. In California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $160,900.00 - $257,100.00 annually. This base pay range is specific to these locations and may not be applicable to other locations.In Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia the standard base pay range for this role is $152,900.00 - $244,300.00 annually. The base pay range is specific to these locations and may not be applicable to other locations. In addition to a competitive base salary this position is also eligible for equity awards based on factors such as experience, performance and location. Actual amounts will vary depending on experience, performance and location. Employees in this role will not be paid below the salary threshold for exempt employees in the state where they reside.

Requirements

• A Ph.D. or equivalent experience in Computer Science or a related field, with a focus on LLM, Agentic System, or Machine Learning
• 2+ years of experience in building large-scale, high-impact ML solutions, particularly in areas such as NLP, agent-based systems, or multi-agent collaboration or similar paradigms
• Experience in developing and working with LLM reasoning models or AI agents capable of multi-step reasoning and context-rich decision-making
• Strong background in rigorous experimental design and evaluation, including benchmark creation, ablation studies, qualitative and quantitative analysis, and principled measurement of reasoning quality, generalization, and safety
• Track record of publishing high-impact research in top AI/ML venues

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