AI Applied Scientist - PhD Intern, Foundational IQ

Zillow

98d • $104,000 - $166,000

About The Position

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.

Requirements

• 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

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

Benefits

• Comprehensive medical, dental, vision, life, and disability coverages
• Parental leave
• Family benefits
• Retirement contributions
• Paid time off

What This Job Offers

• Career Level: Intern
• Education Level: Ph.D. or professional degree
• Number of Employees: 251-500 employees