Applied Research Scientist, World Model

📋 Description

• Design, develop, and advance a foundational housing market model integrating supply, demand
• Transform large-scale shopper behavior data into structured demand representations, estimate
• Design and train latent state-space models, multi-tower architectures, and sequential dynamics
• Develop models supporting forward-looking scenarios (e.g., listing price impact on selling
• Estimate home-level demand curves, willingness-to-pay distributions, and market-clearing dynamics
• Apply causal inference techniques to observational data, addressing selection bias and determining

🎯 Requirements

• PhD in Computer Science, Machine Learning, Statistics, Economics, or a related quantitative
• Hands-on experience building latent state-space models or world models (e.g., RSSM/Dreamer
• Demonstrated ability to transform large-scale behavioral data into structured demand
• Practical experience with causal inference methods on observational data (quasi-experiments
• Strong programming skills in Python and practical experience with PyTorch or TensorFlow.
• Proven track record of independently delivering open-ended research projects.

🎁 Benefits

• Competitive compensation with salary ranges: $195,600-$312,400 in CA, CT, MD, MA, NJ, NY, WA, DC
• Eligibility for equity awards in addition to base salary.
• Fully remote work from anywhere within the United States.
• Distributed work culture with flexibility and trust.
• Research ownership and influence on foundational AI research.
• Professional growth in machine learning, causal inference, and economic modeling.