About the Role
Develop and maintain machine learning models that power automated home pricing, working closely with data scientists and engineers to improve accuracy, scalability, and deployment speed.
Responsibilities
- Build and deploy machine learning models for real-time home price estimation
- Collaborate with data science teams to integrate new pricing features
- Improve model performance through feature engineering and algorithm refinement
- Work with large-scale real estate datasets to train and validate models
- Optimize model inference speed and reliability in production environments
- Monitor model behavior and implement safeguards against drift
- Support A/B testing to evaluate pricing strategies
- Ensure pricing models comply with regulatory and business requirements
- Diagnose and resolve issues in model pipelines and data flows
- Contribute to documentation and knowledge sharing across teams
Nice to Have
- Master’s degree or PhD in a quantitative field
- Experience in real estate, pricing, or marketplace domains
- Background in econometrics or forecasting models
- Contributions to open-source machine learning projects
- Experience deploying models in production at scale
Compensation
Competitive salary and equity package based on experience and location
Work Arrangement
Hybrid work model with office and remote flexibility
Team
Part of the pricing technology team focused on building accurate, scalable valuation systems
About the Team
This team develops the core machine learning infrastructure behind automated home pricing, combining engineering rigor with data science to deliver fast, reliable valuations.
Impact
Your work will directly influence how homes are priced across multiple markets, enabling faster transactions and better customer outcomes.
Available for qualified candidates on a case-by-case basis
