Why This Role Stands Out
As a Principal Machine Learning Engineer at Zillow, you'll spearhead the development of cutting-edge agentic AI systems that redefine the real estate experience, offering significant opportunities for technical leadership and impact. This remote role is ideal for experienced ML leaders passionate about building scalable, innovative solutions within a collaborative and inclusive culture. You'll thrive here if you enjoy shaping the future of AI-driven products and mentoring talented engineers.
Quick Overview
Job Description
Zillow is seeking a Principal Machine Learning Engineer to lead agentic AI systems that power our real estate marketplace. You will design, build, and deploy large-scale ML and LLM-based agents for search, recommendations, pricing, and customer experiences. Partner with data science, product, and engineering to define technical strategy, architecture, and best practices. Mentor engineers, drive experimentation, and ensure robust, reliable ML pipelines. Help shape Zillow's next generation of AI-driven products that transform how people find, rent, and buy homes in a collaborative, flexible, and inclusive culture.
Responsibilities
- Design and lead architecture for large-scale ML and agentic AI systems powering Zillow's marketplace.
- Develop, deploy, and maintain production ML and LLM-based agents for search, recommendation, and pricing.
- Collaborate with data science, product, and engineering to define AI strategy and translate business needs into technical solutions.
- Establish best practices for MLOps, experimentation, and model governance for reliability and scalability.
- Mentor and guide engineers, fostering a culture of learning, experimentation, and inclusive collaboration.
- Analyze product performance, run A/B tests, and iterate on models to improve customer experience and business impact.
Required Skills
- Machine learning
- Deep learning
- Large language models (LLMs)
- Reinforcement learning / agentic AIPython
- ML infrastructure (Kubeflow, Airflow, or similar)
- Cloud platforms (AWS, GCP, or Azure)
- MLOps and CI/CD for MLData engineering with big data tools
- Experimentation and A/B testing
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