Machine Learning Engineer

Opendoor • Toronto, ON

111d

About The Position

Opendoor is hiring a senior-level-or-above Machine Learning Engineer for its ML team, which works to automate and improve decisions across the product. These are builder roles across the machine-learning stack: developing models for business-critical areas such as pricing, risk, repairs, and decision optimization; building intelligent services that support decision-making in real estate; or creating platforms that accelerate model learning and the broader ML lifecycle.

The engineer will work closely with researchers, product, and operations to build agile, accurate, and resilient automation for real-world operations. The role calls for end-to-end ownership, sound technical judgment, comfort with ambiguity, and the ability to connect technical work to product outcomes. Opendoor describes its culture as process-light and high-trust, with reviews, proposals, and demos rather than project managers or scrum.

Requirements

• Senior-level experience or above shipping and operating production ML systems, ML-adjacent services, or data/ML platforms.
• Strong fundamentals in Python and comfort learning new programming languages.
• Proficiency in statistics and distributional reasoning, with practical experience monitoring real-world ML systems.
• Expertise across the ML lifecycle, including training, evaluation, deployment, monitoring, and iteration.
• Experience with associated ML lifecycle tooling, such as MLflow, Airflow, Spark, or Delta Lake.
• Ability to make and communicate design decisions and tradeoffs with stakeholders.
• Based in or willing to relocate to Miami, Toronto, or Seattle.

Nice To Haves

• ML systems experience in business-critical domains such as pricing, forecasting, logistics, marketplaces, or risk.
• Experience with streaming and event-driven systems such as Kafka, gRPC, Redis, or workflow engines.
• Interest in real estate or other messy, high-stakes domains with imperfect data.

Responsibilities

• Build and train production models for pricing, automation, and decision systems used by customers and involving real financial stakes.
• Collaborate with researchers and analysts to turn prototypes into clean, testable, production-ready code and systems.
• Own model pipelines end to end, including data ingestion, training, validation, versioning, deployment, and monitoring.
• Design, build, and evolve mission-critical services and APIs that connect with real-world operations.
• Build platforms that accelerate the ML lifecycle, including agentic research, automated retraining, experimentation, deployment, and monitoring.
• Address challenges such as sparse data, data drift, and model decay in a volatile market.
• Use AI tools in daily work and help advance their application.
• Lead technical design reviews, mentor teammates, and raise engineering standards.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed