Operations AI Engineer

At Opendoor our mission is to tilt the world in favor of homeowners and those who aim to become one. Homeownership matters. It's how people build wealth, stability, and community. It's how families put down roots, how neighborhoods strengthen, how the future gets built. 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

At Opendoor, AI isn't a side project - it's how we work. Across the company, teams default to AI to solve problems, ship faster, and remove friction from everything we do. The AI Operations team sits at the intersection of data, AI engineering, and operational systems.

You will help define the future of how we work across our Operations by building AI powered workflows, automation, and experiences that drive leverage across Operations. This is high agency work in high ambiguity - turning messy problems into clean solutions, prototyping fast, and shipping.

You'll design systems and influence tooling decisions that affect how our operations scale – so we need people who think critically about what they build, why, the impact it will have on our operations and how to measure success.

We're looking for AI-native builders to join our team, blending Operational expertise and systems thinking to drive leverage across our teams. If you default to AI in how you approach work, you’ll fit right in.

Why Join Us

• Opportunity to shape AI Operations from the ground up
• Work on cutting-edge systems directly impacting our Customers and Operations
• Collaborate with talented engineers, data scientists, product teams, and operational teams

• Turn ambiguous business problems into clear system designs, experiments, and implementation plans
• Identify and implement opportunities to improve our operational reliability, efficiency, and cost
• Prototype and ship rapid experiments that test improvements to workflows
• Evaluate build-vs-integrate decisions for AI tools and automation, with a bias toward scalable, maintainable solutions over one-off demos
• Build and maintain AI-powered automation, internal tools to improve how we work
• Assess and pressure-test system architectures — your own and others' — for scaling limitations, failure modes, and operational sustainability
• Maintain clean, trustworthy data through thoughtful schemas, audits, and dashboards that improve visibility and decision making
• Implement monitoring and alerting for model performance, latency, and drift detection
• Troubleshoot production issues and conduct root cause analysis
• Partner with data scientists and R&D to operationalize models
• Develop runbooks, documentation, and best practices for AI Operations

AI Alchemy: You have high agency and a tinkering mindset. You default to experimenting, running variations with new tools and methods - LLMs, automation platforms, coding assistants until you can transmute data into high quality results. You're comfortable writing SQL, working with APIs, and stitching tools together to ship solutions fast.

Systems Thinking: You understand how data flows, business and systems objectives, how decisions get made, and how changes in one part of an operation ripple across others. You evaluate global vs. local trade-offs and can explain why a solution that works today might break at scale - before someone has to ask.

System Design: You take complex, messy problems and reduce them to simple, durable architecture. You know when to build custom vs. integrate existing tools, and you proactively identify scaling limitations before they become technical debt.

Communication: Articulate problems clearly to both technical and non-technical audiences. You write clean documentation, build dashboards that tell a story, and can walk a stakeholder through your design rationale.

Impact Driven: You optimize for impact over novelty, choosing the right tools for the job rather than chasing trends with a long range horizon, observability to ensure performance, scalability and resiliency

Additionally

• Familiarity with LLM deployment, context engineering and comfortable writing SQL
• Understanding of machine learning lifecycle concepts: training, evaluation, deployment, monitoring
• 5+ years of experience in operations and data experience

You'll work across a range of tools and we expect this list to evolve. Current stack includes:

• AI coding assistants: Claude Code, Cursor, or similar
• Rapid prototyping tools: Lovable, Replit, Figma
• Workflow automation: Gumloop, Zapier, or similar platforms
• Experience with MCP integrations is a plus
• Experience with data platforms (Snowflake, Databricks) is a plus
• Background in agentic AI frameworks or building automation flows is a plus

You don't need deep expertise in all of these, but experience in one or more is a strong signal

• Pricing and valuation
• Sales,support, partnerships
• Home operations
• Homes support