Machine Learning Engineer

Zillow

Machine Learning Engineer contributing to large-scale machine learning systems for Zillow's rich media experiences. Collaborating with teams to optimize models and enhance real estate understanding for customers.

Posted 6/2/2026full-timeRemote • 🇩🇪 GermanyJuniorWebsite

Tech Stack

Tools & technologies

AirflowAWSCloudGoGoogle Cloud PlatformKubernetesPythonPyTorchSparkTensorflowTypeScript

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonPyTorchJAXTensorFlowAWSGCPKubernetesSparkAirflowTypeScript

Soft Skills

collaborationengineering judgmentcommunicationinfluenceproblem-solvingadaptabilityleadershipsupportreliabilityinnovation