Distinguished Scientist

About the team

The Agentic AI team at Zillow is at the forefront of transforming the real estate industry by helping millions of people use advanced AI technologies across every step of their home-shopping and financing journey. We are building proactive, always-on assistants that integrate deep user understanding, market intelligence, and sophisticated reasoning to guide people from initial intent through closing and beyond. By embedding domain knowledge and specialized expertise into these assistants, we empower industry professionals and consumers to work more efficiently while preserving Zillow’s competitive edge and trusted brand.

Our team brings together applied scientists, machine learning engineers, and software engineers who own the full lifecycle of AI experiences—from data collection and foundation model training to agentic skill design, multi-agent orchestration, evaluation, and large-scale deployment. We operate in a highly collaborative environment with AI platform, finance, and product teams, and we rely on Zillow’s unique, privacy-preserving real‑estate and transaction datasets to build capabilities that few other companies can match.

In this role, you will define and drive the technical vision for long‑horizon, supervised agentic systems  will design agent architectures, memory and supervision frameworks, and learning strategies that allow our agents to build and maintain context‑rich relationships over weeks and months, not just single sessions, while remaining trustworthy, compliant, and measurably helpful. You will leverage Zillow’s uniquely rich domain datasets—such as interaction logs, behavioral signals, and structured real‑estate and transaction data—to train and adapt agents that make high‑quality, human‑caliber recommendations at scale.

• Own the end-to-end research and technical strategy for long‑horizon agentic experiences across shopping, financing, and professional workflows, in close partnership with the Agentic AI, data platform, and product teams.
• Design and advance LLM post‑training and evaluation methods (e.g., SFT, preference learning, RLHF/RLAIF, long‑context modeling) tailored to supervised, high‑stakes journeys in a complex, regulated domain.
• Architect systems that combine persistent memory, tool use, and multi‑agent collaboration to deliver consistent, context‑rich guidance over long timelines.
• Translate Zillow’s heterogeneous data (text, voice, behavioral, and structured real‑estate/transaction data) into agent‑ready knowledge and signals, in partnership with data and platform teams.
• Collaborate with product and design to define success metrics, evaluation frameworks, and experiment plans for agentic experiences, including human‑in‑the‑loop supervision and safety reviews.
• Operate as a senior IC with the option to lead a small pod (up to ~5 scientists/engineers) focused on long‑horizon agentic systems, mentoring principal‑level talent and setting a high technical bar.
• Represent Zillow’s work externally through publications, talks, open‑source contributions, and thoughtful engagement with the research community, helping position Zillow as a destination for top agentic AI talent.

You will partner closely with senior leaders to align foundational research, platform investments, and product roadmaps around a coherent vision for Zillow as an AI‑native company.

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 $ – $ annually.

This base pay range is specific to these locations and may not be applicable to other locations. In Colorado, Hawaii, Illinois, Minnesota, Nevada, Ohio, Rhode Island, and Vermont the standard base pay range for this role…