Machine Learning Engineer

About the team As a Machine Learning Engineer within Zillow ’s Rich Media Virtual Staging AI team, you’ll join a group focused on helping people better understand homes through immersive, AI-powered experiences. The team works on turning photos, video, and spatial signals into structured representations that power customer-facing products used by millions of shoppers. Within Rich Media, the VSAI team is building systems that transform home media into products that enrich the understanding of a home. About the role This is a high-impact individual contributor role for someone who loves operating at the intersection of modeling and systems. As a Machine Learning Engineer, you’ll help shape how Zillow builds production-grade machine learning systems for rich media experiences, partnering across applied science and engineering to turn promising ideas into reliable, scalable product capabilities. You Will Get To: - Productionalization: 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 that make our services reliable and give scientists insight into the performance of their models in production. Designing services to expose ML models to Zillow ’s end customers - Data: Good data is key to many SOTA ML methods. You will own our team’s datasets, lead and support data engineering projects, understand datasets from other teams, and collaborate with scientists and other teams to prepare them for model training. - Training & Experimentation: Owning projects and supporting scientists in running large-scale training and data processing by collaborating with them on specific projects, establishing generalized best practices, and sharing expertise around performance and software engineering principles, while leveraging AI coding and productivity tools. - Modeling: Staying on top of cutting-edge research (for example, on platforms like Arxiv, X, and Papers With Code) and modifying its methods for our use cases in innovative ways to enable new product experiences or improve existing ones. - Dev & MLOps: Establishing best practices around code quality, testing, and ownership that allow us to move fast without compromising reliability (and sleep). Participating in our existing on-call rotation This role has been categorized as a Remote position. “Remote” employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice, which must be identified to the Company. U.S. employees may live in any of the 50 United States, with limited exceptions.In addition to a competitive base salary this position is also eligible for equity awards based on factors such as experience, performance and location. Actual amounts will vary depending on experience, performance and location. Employees in this role will not be paid below the salary threshold for exempt employees in the state where they reside. Who you are - You have 1-3 years professional experience building and shipping machine learning models or ML-powered systems in production. - You have strong hands-on proficiency in Python and at least one modern machine learning framework, such as PyTorch, JAX or TensorFlow. - You have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes) - You have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems) - You have experience using backend code languages such as TypeScript or Go to fully implement ML-powered systems end-to-end - You have experience building and operating end-to-end machine learning workflows, including data pipelines, model training, evaluation, deployment, and monitoring. - You have a strong foundation in machine learning fundamentals such as representation learning, structured prediction, compute