Job description

About the team 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 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 are Must-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. 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.

Requirements

Must-haves

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.
Who you are Must-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.
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.
Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable state and local law.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmachine learningpythonml

Platform: workday
Effort: long