Zillow is seeking a Senior Machine Learning Engineer to join its Rich Media Experiences team, focusing on building production-grade ML systems for immersive home exploration experiences. The role involves leading end-to-end ML workflows—from data and training to deployment and monitoring—for products like Instant Floor Plans. You will partner with applied scientists and software engineers to integrate computer vision and spatial signals into customer-facing features.
Responsibilities
Design, build, and operate production-grade machine learning systems that move from prototypes to reliable customer-facing services.
Lead end-to-end machine learning work spanning data, training, evaluation, deployment, observability, and iteration in production.
Partner closely with applied scientists and software engineers across backend, web, and mobile to integrate modern ML techniques into Zillow experiences.
Improve the quality, latency, reliability, and maintainability of machine learning workflows for floor plan and rich media products.
Drive technical decisions in ambiguous problem spaces, especially where structured inference, computer vision, spatial signals, or performance tradeoffs matter.
Establish shared patterns, tooling, and best practices; mentor peers through technical execution, code review, and debugging.
Requirements
Significant professional experience building and shipping machine learning models or ML-powered systems in production.
Zillow is a prominent real estate marketplace in the United States that provides digital solutions for buying, selling, renting, and financing homes. The company acts as a housing super app, connecting consumers with tools, real estate professionals, and home data like the Zestimate to simplify the real estate transaction experience.
Strong hands-on proficiency in Python and at least one modern machine learning framework, such as PyTorch or TensorFlow.
Experience building and operating end-to-end machine learning workflows, including data pipelines, model training, evaluation, deployment, and monitoring.
Strong foundation in machine learning fundamentals, including representation learning, structured prediction, computer vision, optimization, and failure analysis.
Comfort debugging model and system behavior in real-world environments using metrics, logs, and experiments.
Effective collaboration with applied scientists, software engineers, and product partners in ambiguous, cross-functional settings.
Nice to Have
Experience in computer vision, spatial data, 3D, AR/VR, mapping, search, recommendation systems, or related domains.