AI Applied Scientist - PhD Intern, Foundational AQ & EQ

About the team The Applied Science AQ/EQ (Action/Emotion Quotient ) Foundation team at Zillow is at the forefront of advancing intelligent systems that empower millions of customers for their home shopping journey. Our mission is to build cutting-edge models and agentic workflows that can provide actionable recommendations and execute tasks on behalf of users during one of the most complex and high-stakes financial decisions of their lives. We leverage Zillow’s vast, unique and domain-specific datasets to design adaptive AI systems that integrate with expert workflows and reduce the stress of the home-buying journey. About the role As a PhD Research Intern on the AQ/EQ Foundation team, you will conduct state-of-the-art research in foundational models and building agentic AI systems. You will focus on fine-tuning and reinforcement learning of large language models (LLMs), with an emphasis on customizing them for Zillow’s domain. You’ll also explore the design of automated, agentic workflows that allow intelligent systems to reason, plan, and act in ways that directly improve the home-buying experience. This role offers the opportunity to collaborate with applied scientists, engineers, and product leaders while pushing the boundaries of personalization and intelligent automation. You'll design training algorithms and therefore the improved agentic AI workflow will be shipped to various Zillow Agentic Skills to power the integrated Zillow Copilot experience, which is a rare opportunity to transform the real estate industry. Responsibilities include: Researching and developing techniques for fine-tuning LLMs with domain-specific data Applying reinforcement learning to optimize model performance for user-centric outcome Designing and prototyping agentic workflows that can autonomously perform tasks and assist home buyers Collaborating with cross-functional teams to evaluate and deploy research prototypes Sharing insights through presentations, documentation, and potentially 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 publication record Candidates should have a background in one or more of the following areas: Advanced research in natural language processing (NLP) and/or reinforcement learning (RL) Practical experience fine-tuning and adapting large language models (LLMs) for specific use cases Familiarity with the design and implementation of automated/ agentic workflows Deep understanding of LLMs, hands on experience of post-training with the most popular OSS models Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow) Excited about applying advanced AI methods to impactful, real-world problems Strong communication skills and ability to work collaboratively in a multidisciplinary environment Strong research mindset, with motivation to publish ​Here at Zillow - we value the experience and perspective of candidates with non-traditional backgrounds. We encourage you to apply if you have transferable skills or related experiences. G ... (truncated, view full listing at source)