{"id":1248144,"url":"https://alion.io/job/opendoor-labs-inc-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":2680,"name":"Opendoor","domain":"opendoor.com","url":"https://alion.io/company/opendoor-labs-inc","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"A","score":95,"open_postings":38,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":84,"computed_at":"2026-10-30T05:48:23Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Delta Lake","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"Scrum","optional":false},{"name":"Spark","optional":false},{"name":"Apache Kafka","optional":true},{"name":"gRPC","optional":true},{"name":"Redis","optional":true}],"status":"live","first_seen_at":"2026-07-20T16:52:12Z","employer_posted_date":"2026-07-20","last_verified_at":"2026-10-29T22:19:51Z","board_verified":true,"closed_at":null,"days_open":101,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":100},"description":"About Opendoor\nAt 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.\n\nAbout the Role - Senior and Above\nYou're interviewing for Opendoor’s ML team which seeks to automate and refine every decision made in our product. We don't slot into silos; you'll build where you have the most impact and the most fun.\nThese are builder roles across the ML stack. Wherever you land, you'll be doing one of three things:\nBuilding 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.\nBuilding theintelligent services that bring structured, precise decision-making into the highly unstructured world of real estate.\nBuilding platforms that accelerate how fast our models learn. How fast we learn dictates how fast this company can grow.\nYou'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 are growing fast and this work is at the core.\nThis isn't a role for everyone. We choose hard mode. We're process-light, high-trust, and we don't put artificial boundaries between you and the work. You'll be expected to understand how your piece connects to the product and communicate at that level. We don't have project managers, we don't have scrum. We do reviews, proposals, demos, and trust.\nWhat We're Looking For\nYou ship. You pick the boring solution when boring is right and the novel one when it isn't. You know when “good enough and shipped today” beats “perfect next quarter.”\nYou have high agency. You don't wait for permission or a perfectly scoped ticket. You see the problem, take ownership end-to-end, and pull in whoever you need. Lean teams, significant latitude, real accountability.\nYou run at unclear problems. The most valuable problems here don't come with a playbook - messy data, imperfect ground truth, markets that shift under you. Ambiguity is the job, not an obstacle to it.\nYou hold a high standard. You measure twice and cut once. You review code, raise the bar on everything around you, and treat the quality of our end-to-end judgment as your problem.\nYou think in first principles. You have opinions on architecture, distributed systems, ML lifecycle tradeoffs, and the constraints and tripwires of operating models in a high-stakes environment.\nYou default to AI. You've already integrated modern AI tools into your daily workflow. You use them to move faster, not as a crutch.\nYou communicate well. 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.\nYou believe in what we're building. Not hype, conviction. You see the opportunity in what we're doing and you want to be part of finishing it.\nYou have fun. We stay human when times are hard. The task is daunting, but we're all in it together.\nWhat You'll Do\nBuild and train models that real customers and real money depend on - pricing, automation, and decision systems in production\nWork side-by-side with researchers and analysts to turn prototypes into clean, testable, production-ready code and systems\nOwn model pipelines end-to-end: data ingestion, training, validation, versioning, deployment, and monitoring\nDesign, build, and evolve mission-critical services and APIs that connect to real-world, messy operations\nBuild the platform that accelerates the full ML lifecycle: agentic research, automated retraining, experimentation, deployment, monitoring\nProactively tackle real-world challenges like sparsity, data drift, and model decay in a volatile market\nUse AI tools daily and help push them further than anyone else in the industry\nLead technical design reviews, mentor teammates, and raise the bar on everything around you\nQualifications\nSenior-level or above: deep experience shipping and operating production ML systems, ML-adjacent services, or data/ML platforms\nStrong fundamentals in Python; comfortable picking up new ones\nProficiency with statistics and ability to reason distributionally; has put it to work with real-world monitoring of ML systems\nExpertise with the end-to-end ML lifecycle (training, evaluation, deployment, monitoring, and iteration) and associated tooling (e.g. MLflow, Airflow, Spark, Delta Lake)\nDemonstrated ability to make and communicate design decisions and tradeoffs across stakeholders\nBased in or willing to relocate to Miami, Toronto, or Seattle\nNice to Have\nML systems experience in business-critical domains: pricing, forecasting, logistics, marketplaces, risk\nStreaming and event-driven systems (e.g. Kafka), gRPC, Redis, or workflow engines\nInterest in real estate or other messy, high-stakes domains with imperfect data\nInterview Process\nWe move fast. Typically:\nRecruiter phone screen (15 min)\nA 60 minute technical deep dive to understand a past problem or project you've worked on\nTwo 60 minute pairing-style technical reviews\nWe're not running these to see if you can finish a problem under pressure. We want to know what it's like to work with you. Before each interview you'll receive an email on what to expect.\nNot a perfect fit on paper but clearly excellent? Apply anyway and tell us why in your cover letter. We value T-shaped people. If you have deep expertise in an adjacent area and a strong point of view on how it applies here, that's exactly who we want to talk to.\nAbout Opendoor\nAt 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. 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