Zillow

Distinguished Scientist role focused on defining and driving the technical vision for long-horizon, supervised agentic systems at Zillow. This involves designing agent architectures, memory and supervision frameworks, and learning strategies, leveraging Zillow's real-estate datasets to train and adapt agents for high-quality recommendations. The role requires expertise in LLM post-training, evaluation methods, and architecting systems for persistent memory, tool use, and multi-agent collaboration in a complex, regulated domain.

PhD intern role focused on foundational research in agentic AI systems, involving fine-tuning and reinforcement learning of LLMs for Zillow's domain, with the goal of shipping improved agentic AI workflows to power the Zillow Copilot experience.

PhD Research Intern role focused on training and adapting large models for Zillow's domain, including multimodal understanding, user modeling, and agentic workflows. The role involves research, prototyping, evaluation (offline and online), and collaboration with product and engineering teams, with opportunities for publication.

This role focuses on developing next-generation AI agents for a real estate exploration experience. The agents will be capable of reasoning, planning, and collaborating, utilizing large foundation models, conversational AI, LLMs, and multi-modal inference. Key research areas include personalized home exploration, conversational optimization with context management, and multi-modal collaboration systems integrating 3D computer vision and LLMs.