Applied Scientist, Construction

About the team

The Shopping AI team is at the heart of Zillow Group’s mission to create a seamless digital real estate marketplace. We build and own the machine learning systems that power Zillow’s core user experiences—like personalized search, recommendations, and display optimization—across our apps and websites. Our close-knit group of engineers and scientists collaborates with product and design to apply the latest AI advancements, tackling large-scale challenges and shaping the future of home shopping at Zillow.

As an Applied Scientist, you will help drive the next generation of ranking, recommendation, and generative AI systems for Zillow’s home-shopping experience. Your work will directly impact how millions of users discover and shop for homes, as you research, design, and deploy innovative machine learning models that power key product features. You’ll collaborate across disciplines to deliver solutions that make home shopping more intuitive, personalized, and efficient.

• Research, design, and prototype new machine learning models for Zillow’s consumer products, including search, ranking, recommendations, and notifications.
• Lead the full model lifecycle: from ideation and offline experimentation on large datasets to online deployment, A/B testing, and ongoing performance monitoring.
• Develop and test novel modeling approaches, such as deep learning architectures and large language models (LLMs), to improve the home-shopping experience.
• Apply cutting‑edge AI techniques, including LLM‑based retrieval and RAG‑style systems, to create more natural and conversational property search experiences.
• Optimize how and when homes are displayed to users, ensuring relevance and context in every interaction.
• Collaborate with engineers, product managers, and designers to define, execute, and iterate on impactful projects.
• Partner with data engineering and infrastructure teams to leverage large-scale distributed data systems for feature engineering and model training.

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 $ - $ 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 $ - $ annually. The base pay range is specific to these locations and may not be applicable to other locations. In addition to a competitive base salary this position is also eligible for equity awards based on factors such as experience, performance and location.

Actual amounts will vary depending on experience, performance and location. Employees in this role will not be paid below the salary threshold for exempt employees in the state where they reside.

• Experienced in designing, training, and analyzing machine learning models to solve real-world business problems, not just academic benchmarks.
• Proficient in Python and familiar with common ML libraries (e.g., PyTorch, Tensor Flow, Cat Boost, scikit-learn, Hugging Face) and distributed data systems (e.g., Spark, Hive, Databricks, Airflow).
• Skilled in the scientific lifecycle: hypothesis formation, experiment design, result analysis, and clear communication to technical and non-technical partners.
• Demonstrated experience in at least one of the following: search/information retrieval, personalized ranking/recommender systems, or generative AI/LLM-based systems.
• Holds a PhD or Master’s degree in a relevant field (Computer Science, AI, Data Science, or related), or equivalent industry experience (typically 3-4+ years with a Master’s, or 1+ year with a PhD).
• Comfortable working near production systems and collaborating closely with engineers and cross-functional partners.
• Bring…