About the teamThe 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 Applied Scientist will focus on user modeling - building the user world models that help our agents understand where each shopper is in their journey, what they ultimately want, and how our actions change what happens next. The role will drive complex, ambiguous work end-to-end, partnering closely with product, engineering, science, platform, and data teams to translate the latest advances in sequence modeling, causal inference, and reinforcement learning into production impact. This is an exciting time to join as Zillow is building agentic skills across the full home-shopping journey - from search and personalized guidance to offer strategy and financing - at a scale of hundreds of millions of requests per day.
About the role- Own LLM post-training pipelines - SFT, DPO, and reinforcement fine-tuning (RFT/GRPO) - running training end-to-end on GPU infrastructure.
- Build and train fast, multi-category reward models (PRMs) that emit scalar signals from contrastive preference pairs.
- Design on-policy and online assessment: score live agent trajectories using LLM-as-a-Judge frameworks.
- Translate offline evaluation rubrics into generalizable reward functions that work on arbitrary production traces.
- Independently drive complex work end-to-end by collaborating across partner teams.
- Provide technical leadership and mentorship while helping translate state-of-the-art AI 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 areMust-haves- PhD in Computer Science, Statistics, Economics, Machine Learning, or a related quantitative field, with depth in reinforcement learning or causal inference.
- Hands-on experience modeling user behavior at scale: sequence models over event streams, sessions, or trajectories, and prediction of user intent or long-term outcomes.
- Practical depth in causal inference (propensity scoring, treatment-effect estimation, off-policy evaluation) or reinforcement learning applied to real sequential decision problems.
- Ability to independently scope and solve ambiguous problems end-to-end while providing technical leadership to scientists and MLEs.
- Working proficiency in Python and ML frameworks such as PyTorch or TensorFlow, enough to run your own experiments and to read, review, and debug code.
Nice-to-haves- Background in economics or econometrics, such as demand modeling or consumer choice.
- Published work in RL, causal inference, survival analysis, or user modeling/simulation.
- Experience with generative sequence models or conditional language generation for behavioral/conversational simulation.
- Experience with rare-event modeling and probability calibration at scale.
- Familiarity with two-sided marketplaces, real estate, or long-horizon consumer journeys.
