Principal Applied Scientist – User Modeling

Zillow

. Build user world models that help agents understand each shopper’s journey, goals, and how actions affect outcomes .

Posted 10/20/2026full-timeRemote • United StatesLead💰 $185,800 - $312,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building user models and reinforcement learning, with a strong focus on translating AI research into practical applications. Proficient in technical leadership and collaboration across multidisciplinary teams to drive complex projects.

Highest-signal resume keywords

PhD In Computer ScienceReinforcement Learning ExpertiseHands-On Experience With PythonExperience With ML FrameworksTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

User Behavior ModelingCausal InferenceSequence ModelsPredicting User IntentExperimentationDebugging CodeReward ModelsGenerative Sequence ModelsProbability CalibrationTreatment-Effect Estimation

Soft Skills

Problem SolvingMentorshipCollaboration

Tools & Technologies

PyTorchTensorFlowGPU Infrastructure

Industry Keywords

EconomicsDemand ModelingConsumer ChoiceTwo-Sided MarketplacesReal Estate

Tech Stack

Tools & technologies

PythonPyTorchTensorflow

About the role

Key responsibilities & impact

Build user world models that help agents understand each shopper’s journey, goals, and how actions affect outcomes
Own LLM post-training pipelines, including SFT, DPO, and reinforcement fine-tuning (RFT/GRPO), end-to-end on GPU infrastructure
Build and train multi-category reward models (PRMs) that emit scalar signals from contrastive preference pairs
Design on-policy and online assessment using LLM-as-a-Judge frameworks to score live agent trajectories
Translate offline evaluation rubrics into generalizable reward functions for arbitrary production traces
Drive complex, ambiguous work end-to-end in collaboration with product, engineering, science, platform, and data teams
Provide technical leadership and mentorship
Translate state-of-the-art AI research into production impact for Zillow’s home-shopping experiences

Requirements

What you’ll need

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 using sequence models over event streams, sessions, or trajectories
Experience predicting user intent or long-term outcomes
Practical depth in causal inference, including propensity scoring, treatment-effect estimation, and off-policy evaluation, or reinforcement learning applied to real sequential decision problems
Ability to independently scope and solve ambiguous problems end-to-end
Ability to provide technical leadership to scientists and MLEs
Working proficiency in Python and ML frameworks such as PyTorch or TensorFlow
Ability to run experiments and read, review, and debug code
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

Benefits

Comp & perks

Equity awards based on factors such as experience, performance and location
Remote work from a physical location of choice within the eligible U.S. locations
Flexible work through Cloud HQ
Equal employment opportunity and accommodation support