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About the team
Zillow has the richest housing market data in the industry. You help us build out a foundational model that leverages a representation of who is in the market, what they’ll pay, and how that demand clears against supply.
The Agentic Foundations team is a focused group of 15–20 applied scientists within Zillow’s broader Agentic AI organization, building the foundational AI technologies that power the next generation of home shopping experiences. This Principal Applied Scientist will help build the Zillow World Model: a single, uncertainty-aware model of the housing market that ties together supply, demand and macro conditions such as mortgage rates and policy. Today, bespoke models like Zestimate, Time-to-Pending and Listing Soon each answer one question well. The foundational “World” Model aims to answer the multi-step “what if” questions consumers actually ask, such as “If I list at $500K, when will it sell?” or “I want at least $800K; what should I list at?”, as consistent projections of one market state, and to serve those answers to Voyager, Zillow’s AI Mode, as a tool.
This is a very new field and an open-ended research effort. You will partner with the World Model working group (listing-state modeling, market simulation and evaluation) and with product, engineering, platform and data teams. Zillow observes both sides of the housing market at a depth no one else can, so this work can directly help guide users toward their goals. About the role
Build the demand layer of the World Model: turn large-scale shopper behavior (search, engagement, session trajectories) into structured demand representations, including explicit attention-to-demand conversion.
Design and train latent state-space, multi-tower and/or sequential dynamics models with calibrated uncertainty that support multi-step rollouts, forward planning (“if I do X, what happens?”) and backward planning (“I want X; what should I do?”).
Estimate willingness-to-pay, per-home demand curves and market clearing dynamics, connecting learned representations to economic quantities.
Apply causal identification in observational data and define where the model must abstain because a correlation does not support a “what-if” claim.
Contribute to the World Model evaluation battery: state probes, calibrated multi-target forecasts, rollout drift, causal checks and out-of-regime replays.
Independently drive ambiguous research end-to-end, and provide technical leadership and mentorship in translating state-of-the-art research into production impact.
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 $195,600.00 - $312,400.00 annually. This base pay range is specific to these locations and may not be applicable to other locations.In Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia the standard base pay range for this role is $185,800.00 - $296,800.00 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. Who you are
Must-haves
PhD in Computer Science, Machine Learning, Statistics, Economics, or a related quantitative field.
Hands-on experience building latent state-space or world models (RSSM/Dreamer-family, JEPA-style latent prediction, or event-sequence transformers): belief-state representations, stochastic transition models with calibrated uncertainty, and evaluation of multi-step rollouts, not just single-target forecasting.
Demonstrated ability to turn large-scale user behavior data into structured demand representations (utility or willingness-to-pay estimation, funnel/conversion modeling) while correcting for selection bias in platform-observed populations.
Practical causal inference in observational settings (quasi-experiments, instruments, policy discontinuities), and judgment about when a learned correlation does and does not support a counterfactual claim.
Proven record of delivering on open-ended research projects, with the ability to scope ambiguous problems and provide technical leadership to scientists and MLEs.
Strong programming skills, especially Python, plus experience with ML frameworks such as PyTorch or TensorFlow.
Nice-to-haves Economics fluency (strongly preferred): market microstructure, search-and-matching, or structural/discrete-choice demand models; reasoning about equilibrium feedback.
Experience with agent-based or market simulation, or simulation-based reinforcement learning.
Published work in world models, model-based RL, causal inference or structural demand estimation.
Experience with multimodal representation learning or bitemporal, leakage-safe training data.
Familiarity with two-sided marketplaces, real estate, or long-horizon consumer journeys.
Get to know us
At Zillow, we’re reimagining how people move—through the real estate market and through their careers. As the most-visited real estate platform in the U.S., we help people navigate buying, selling, financing and renting with greater ease and confidence. Whether you're working in tech, sales, operations, or design, you’ll be part of a company reshaping an industry and helping more people make home a reality.
How we work is a key part of that transformation. Through Cloud HQ, our strategic approach to distributed work, most employees have the flexibility to work from wherever they’re most productive—enabling us to move fast, stay connected and deliver on our people promise.
Zillow is honored to be recognized among the best workplaces in the U.S. Zillow was named one of FORTUNE 100 Best Companies to Work For® in 2026, and included on TIME’s America’s Best Companies 2026 list, reflecting our commitment to creating an innovative, inclusive, and engaging culture where employees are empowered to grow.
No matter where you sit in the organization, your work will help drive innovation, support our customers, and move the industry—and your career—forward, together.
Zillow Group is an equal opportunity employer committed to fostering an inclusive, innovative environment with the best employees. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please contact your recruiter directly.
Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable state and local law.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
