AI Applied Scientist - PhD Intern, Foundational IQ

About the team Zillow AI’s Foundational IQ group builds the core intelligence that powers search, discovery, and conversational experiences like Zillow Copilot. We combine text, imagery, geospatial and time‑series signals to learn rich representations of homes, neighborhoods, and shopper intent, then use them to improve user experiences on Zillow. Our team operates as applied researchers who ship: we prototype quickly, evaluate rigorously (offline metrics and online experiments), and partner closely with product and engineering to bring models to production. We care deeply about helpfulness, safety, and fairness, including domain‑specific, fair‑housing‑aware evaluation, and we invest in modern ML platforms, data governance, and observability to run at national scale. The result is AI that reduces friction, increases confidence, and makes complex home decisions feel simpler for millions of people. About the role As a PhD Research Intern on the Foundational IQ team, you will help train and adapt large models that better understand homes and users, advancing representation learning, multimodal modeling, user modeling, and reinforcement/sequential decision‑making for real‑world problems at Zillow scale. You’ll tailor and evaluate LLMs and multimodal foundation models to our domain, build agentic workflows that plan and act across multi‑step tasks, and define success via domain‑specific metrics emphasizing helpfulness, safety, and fairness. You’ll move quickly from prototype to impact, running rigorous offline evaluations and online experiments, collaborating with applied scientists, engineers, and product partners, and contributing to platform capabilities that power experiences like Zillow Copilot. Along the way you’ll author clear research docs, share results internally, and have opportunities to publish and present your work. Responsibilities include: Research and develop methods for adapting LLMs and foundation models with Zillow’s domain-specific data Build and evaluate multimodal models that combine text, images, geospatial and tabular signals for home and user understanding. Explore reinforcement learning and sequential decision-making for long‑horizon, user‑centric outcomes Prototype agentic workflows; define success metrics and run rigorous offline/online evaluations Partner across science, engineering, product, and design; share results via docs, presentations, and publications 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 $104,000.00 - $166,000.00 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 is $104,000.00 - $166,000.00 annually. The base pay range is specific to these locations and may not be applicable to other locations. Who you are Currently enrolled in a PhD program in Computer Science, Machine Learning, Artificial Intelligence or a related field with a strong research track record Experience in one or more of the following: LLMs: instruction tuning/fine‑tuning, prompting, and evaluation/measurement Multimodal learning (image text; familiarity with audio or geospatial a plus) Representation learning with limited labels (self/semi/weakly-supervised) User modeling for personalization systems Reinforcement learning or sequential decision‑making Evaluating generative/agentic systems; privacy‑aware and responsible AI practices (e.g., fair‑housing considerations) are a plus Proficiency in Python and m ... (truncated, view full listing at source)