Applied Research Scientist – World Model

🔥 12 hours ago

🇺🇸 United States – Remote

💵 $185.8k - $312.4k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧬 Research Scientist

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Zillow

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 2006

🏠 Real Estate

🛍️ eCommerce

👥 B2C

💰 $4.1M Post-IPO Equity on 2012-12

Real Estate • eCommerce • B2C

Zillow is a leading real estate and property rental marketplace that provides comprehensive information on homes, apartments, and properties for sale or rent. It offers users tools to search for properties, calculate mortgage rates, and connect with real estate agents. The platform also features innovative algorithms that provide Zestimates, which are estimated market values of homes. Zillow is a go-to resource for individuals looking to buy, sell, or rent properties, as well as for agents and brokers who want to reach a wider audience.

📋 Description

• Build the demand layer of Zillow’s World Model from large-scale shopper behavior data • Convert search, engagement, and 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 • Support multi-step rollouts, forward planning, and backward planning • Estimate willingness-to-pay, per-home demand curves, and market-clearing dynamics • Connect learned representations to economic quantities • Apply causal identification to observational data • Define where the model must abstain when correlations do not support “what-if” claims • Contribute to the World Model evaluation battery, including state probes, calibrated multi-target forecasts, rollout drift, causal checks, and out-of-regime replays • Independently drive ambiguous research projects end-to-end • Provide technical leadership and mentorship while translating state-of-the-art research into production impact • Partner with the World Model working group and product, engineering, platform, and data teams

🎯 Requirements

• PhD in Computer Science, Machine Learning, Statistics, Economics, or a related quantitative field • Hands-on experience building latent state-space or world models, including RSSM/Dreamer-family, JEPA-style latent prediction, or event-sequence transformers • Experience with belief-state representations, stochastic transition models with calibrated uncertainty, and multi-step rollout evaluation • Ability to turn large-scale user behavior data into structured demand representations, including utility or willingness-to-pay estimation and funnel/conversion modeling • Experience correcting for selection bias in platform-observed populations • Practical causal inference in observational settings, including quasi-experiments, instruments, and policy discontinuities • Judgment about when learned correlations support counterfactual claims • Proven record delivering open-ended research projects • Ability to scope ambiguous problems and provide technical leadership to scientists and MLEs • Strong programming skills, especially Python • Experience with ML frameworks such as PyTorch or TensorFlow • Economics fluency in market microstructure, search-and-matching, or structural/discrete-choice demand models is strongly preferred • 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

🏖️ Benefits

• Equity awards based on factors such as experience, performance and location • Remote work from a physical location of the employee’s choice • Flexibility to work from wherever most productive • Accommodation support for disabilities or special needs