About the job
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.
Responsibilities
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
Qualifications
Minimum
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 modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face)
Clear communication and a collaborative mindset; motivated to publish at top venues
Preferred
Evaluating generative/agentic systems; privacy-aware and responsible AI practices (e.g., fair-housing considerations) are a plus