Machine Learning Engineer

This is a fully remote position, open to applicants in Germany.

📋 Description

• Taking charge of the transition from research code to production-ready and optimized models.

• Creating CI/CD pipelines that enable scientists to deploy models in quick iteration cycles.

• Enhancing our current monitoring systems to ensure service reliability and provide scientists with insights into model performance in production.

• Designing services that make ML models accessible to Zillow’s end customers.

• Managing the team’s datasets, leading and supporting data engineering projects, and collaborating with scientists on model training.

• Assisting scientists in executing large-scale training and data processing projects.

• Establishing best practices regarding code quality, testing, and ownership for enhanced reliability.

• Participating in the existing on-call rotation.

• Keeping current with cutting-edge research and adapting methods for practical application.

• Collaborating across applied science and engineering teams to transform ideas into scalable product capabilities.

⛳️ Requirements

• 1-3 years of professional experience in building and deploying machine learning models or ML-powered systems in production.

• Strong hands-on expertise in Python and at least one modern machine learning framework, such as PyTorch, JAX, or TensorFlow.

• Practical experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes).

• Experience with data engineering tools and constructing robust data pipelines (e.g., Spark, Airflow, streaming systems).

• Proficiency in backend programming languages such as TypeScript or Go to fully implement ML-powered systems from start to finish.

• Experience in building and managing end-to-end machine learning workflows, including data pipelines, model training, evaluation, deployment, and monitoring.

• Strong grounding in machine learning fundamentals, including representation learning, structured prediction, computer vision, optimization, and failure analysis.

• Ability to debug model and system behavior in real-world scenarios and utilize metrics, logs, and experiments to enhance outcomes.

• Effective collaboration with applied scientists, software engineers, and product partners in ambiguous, cross-functional environments.

• Strong engineering judgment and the capability to balance experimentation with reliability, speed, and long-term maintainability.

• Clear communication of technical concepts and the ability to influence decisions across various disciplines.

🏝️ Benefits

• Equity awards based on factors such as experience, performance, and location.

• Competitive base salary.

• Flexible work arrangements.

• Opportunities for professional development.