Machine Learning Engineer

🕒 June 2

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Zillow

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 2006

🏠 Real Estate

🛍️ eCommerce

👥 B2C

💰 $4.1M Post-IPO Equity on 2012-12

Real Estate • eCommerce • B2C

Zillow is a leading real estate and property rental marketplace that provides comprehensive information on homes, apartments, and properties for sale or rent. It offers users tools to search for properties, calculate mortgage rates, and connect with real estate agents. The platform also features innovative algorithms that provide Zestimates, which are estimated market values of homes. Zillow is a go-to resource for individuals looking to buy, sell, or rent properties, as well as for agents and brokers who want to reach a wider audience.

📋 Description

• owning the transition from research code to production-ready and optimized models • establishing CI/CD pipelines that allow scientists to deploy models in short iteration cycles • innovating upon our existing monitoring systems to make our services reliable and give scientists insight into performance of models in production • designing services to expose ML models to Zillow’s end customers • owning team’s datasets, leading and supporting data engineering projects, and collaborating with scientists on model training • supporting scientists in running large-scale training and data processing projects • establishing best practices around code quality, testing, and ownership for reliability • participating in existing on-call rotation • staying updated on cutting-edge research and modifying methods for practical use • collaborating across applied science and engineering teams to turn ideas into scalable product capabilities

🎯 Requirements

• 1-3 years professional experience building and shipping machine learning models or ML-powered systems in production • strong hands-on proficiency in Python and at least one modern machine learning framework, such as PyTorch, JAX or TensorFlow • hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes) • experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems) • experience using backend code languages such as TypeScript or Go to fully implement ML-powered systems end-to-end • 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 such as representation learning, structured prediction, computer vision, optimization, and failure analysis • ability to debug model and system behavior in real-world environments and use metrics, logs, and experiments to improve outcomes • effective collaboration with applied scientists, software engineers, and product partners in ambiguous, cross-functional settings • strong engineering judgment and ability to balance experimentation with reliability, speed, and long-term maintainability • clear communication of technical ideas and ability to influence decisions across disciplines.

🏖️ Benefits

• equity awards based on factors such as experience, performance and location • competitive base salary • flexible work arrangements • professional development