Machine Learning Engineer
Required skills
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 such as 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, comfortable debugging model and system behavior in real-world environments and using metrics, logs, and experiments to improve outcomes, collaborate effectively with applied scientists, software engineers, and product partners in ambiguous, cross-functional settings, strong engineering judgment and know how to balance experimentation with reliability, speed, and long-term maintainability, communicate technical ideas clearly and can influence decisions across disciplines
Preferred skills
Experience in computer vision, spatial data, 3D, AR/VR, or related domains
Technologies
Python, PyTorch, JAX, TensorFlow, AWS, GCP, Kubernetes, Spark, Airflow, TypeScript, Go
Responsibilities
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 existing monitoring systems that make 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, owning the team's datasets, leading and supporting data engineering projects, understanding datasets from other teams, collaborating with scientists and other teams to prepare them for model training, owning projects and supporting scientists in running large-scale training and data processing, establishing generalized best practices, sharing expertise around performance and software engineering principles, leveraging AI coding and productivity tools, staying on top of cutting-edge research and modifying methods for use cases, establishing best practices around code quality, testing, and ownership, participating in existing on-call rotation
Seniority
1-3 years professional experience
Domain
Machine Learning, Rich Media, Virtual Staging, Real Estate
