It's how people build wealth, stability, and community. We're building the modern system of homeownership giving people the freedom to buy and sell on their own terms. We’ve built an end-to-end online experience that has already helped thousands of people and we’re just getting started.

About The Role — Senior And Above You're interviewing for Opendoor’s ML team which seeks to automate and refine every decision made in our product. These are builder roles across the ML stack. Building models in business-critical contexts like pricing, risk, repairs, and decision optimization.

Leverage frontier techniques to extend our capabilities into the unstructured world of real estate. Building the intelligent services that bring structured, precise decision-making into the highly unstructured world of real estate. Building platforms that accelerate how rapid our models learn.

You’ll work directly with researchers, product, and operations to build the automation that scales in the real world. Our systems must be agile, accurate, and resilient in a heterogeneous space. We don’t have project managers, we don’t have scrum.

You ship. You know when “good enough and shipped today” beats “perfect next quarter.” The most valuable problems here don't come with a playbook — messy data, imperfect ground truth, markets that shift under you. You review code, raise the bar on everything around you, and treat the quality of our end-to-end judgment as your problem.

You have opinions on architecture, distributed systems, ML lifecycle tradeoffs, and the constraints and tripwires of operating models in a high-stakes environment. You default to AI. You’ve already integrated modern AI tools into your daily workflow.

You write clear design docs, give useful code reviews, push back on bad ideas without making it personal,



and can land a technical tradeoff with a non-technical stakeholder. You believe in what we're building. Build and train models that real customers and real money depend on — pricing, automation, and decision systems in production Own model pipelines end-to-end: data ingestion, training, validation, versioning, deployment, and monitoring Build the platform that accelerates the full ML lifecycle: agentic research, automated retraining, experimentation, deployment, monitoring Proactively tackle real-world challenges like sparsity, data drift, and model decay in a volatile market Use AI tools daily and help push them further than anyone else in the industry Lead technical design reviews, mentor teammates, and raise the bar on everything around you Senior-level or above: deep experience shipping and operating production ML systems, ML-adjacent services, or data/ML platforms Strong fundamentals in Python; comfortable picking up new ones has put it to work with real-world monitoring of ML systems Expertise with the end-to-end ML lifecycle (training, evaluation, deployment, monitoring, and iteration) and associated tooling (e.g.

Based in or willing to relocate to Miami, Toronto, or Seattle ML systems experience in business-critical domains: pricing, forecasting, logistics, marketplaces, risk Interest in real estate or other messy, high-stakes domains with imperfect data Recruiter phone screen (15 min) A 60 minute technical deep dive to understand a past problem or project you've worked on Two 60 minute pairing-style technical reviews We're not running these to see if you can finish a problem under pressure. It's how people build wealth, stability, and community. We're building the modern system of homeownership, giving people the freedom to buy and sell on their own terms.

We've built an end-to-end online experience that has already helped thousands of people — and we're just getting started. #

📌 Machine Learning Engineer (Python / Data Science) (Toronto)
🏢 Opendoor
📍 Toronto