Applied Scientist AI/ML

About the Role

We’re looking for an Applied Scientist (ALL LEVELS) to push the boundaries of applied machine learning and AI at Opendoor. While this role will have a significant impact on our valuation systems — ensuring we provide the most accurate and transparent pricing possible — the scope goes well beyond pricing. You’ll work across a range of challenging ML problems, from multi-modal modeling to operational optimization, helping us rethink how we use structured and unstructured data to make better decisions for our customers.

What You'll Need

• Strong software engineering and coding skills in Python, with experience contributing to production codebases
• Experience developing and deploying ML models end-to-end — from research and prototyping to implementation in production systems
• Hands-on experience with deep learning architectures, including ConvNets, Transformers, or similar
• Advanced degree (MS or PhD) in computer science, statistics, mathematics, or a related quantitative field
• Solid foundation in statistics and experimental design
• Strong communication and collaboration skills — you’re comfortable working with cross-functional stakeholders and can communicate technical ideas clearly

Nice to Have

• Familiarity with Pyspark and distributed data processing
• Background in search, recommendation systems, or personalization
• Experience working with large language models (LLMs) or vision-language models (VLMs)
• A genuine interest in real estate — no prior experience required, but you'll engage deeply with housing data

What You'll Do

• Design and deploy architectural improvements to our deep neural network (DNN)-based home valuation models
• Build interpretable ML models that can help us explain pricing decisions to customers
• Incorporate unstructured data — like images, videos, or text — into our forecasting and valuation pipelines using cutting-edge AI models (LLMs, VLMs, etc.)
• Collaborate with Engineering and Ops to enhance our human-in-the-loop pricing systems
• Improve the feature engineering and model training pipelines that power our production systems
• Rethink our risk and optimization models using real-world data and domain insight
• We’re a small, nimble team — there’s ample opportunity to work across the entire research and modeling stack.

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