ML Engineer
Senior Machine Learning Engineer leading the design and implementation of scalable, production‑grade AI infrastructure, bridging data platforms and AI teams. Focus on building robust pipelines, MLOps, and cloud services using Python, deep learning frameworks, AWS, Kubernetes, Spark, and SQL.
About the team
About the role
As a Senior Machine Learning Engineer on the Agentic Data Foundations team, you will:
Architect and Build: Lead the design, prototyping, and deployment of scalable machine learning infrastructure that can power Zillow AI Mode.
Bridge and Partner: Serve as a critical technical liaison between the Agentic Data Platform and applied AI teams. You will collaborate closely with scientists, engineers, and product partners to understand their dependencies and deliver the data foundations they need to innovate rapidly.
Influence and Align: Act as a technical anchor for the team. You will drive alignment on timelines, architectures, and prioritization across different organizations, successfully influencing technical direction without requiring direct authority.
Scale for Impact: Deploy and optimize capabilities in production environments, ensuring high availability, low latency, and efficient resource utilization capable of handling massive scale.
Mentor and Elevate: Foster a culture of rapid innovation, frugality, and engineering excellence. You will mentor other engineers, guiding them in using the right technologies and establishing best practices.
Distill Complexity: Translate complex ML infrastructure designs and ambiguous customer problems into clear, actionable insights for diverse audiences, including leadership and non-technical stakeholders.
Who you are
You are a seasoned engineer who seamlessly marries state-of-the-art machine learning paradigms (LLMs, Agentic frameworks) with large-scale data engineering. You love the architectural tension of a rapidly evolving space, expertly balancing the need to ship adaptable, high-value capabilities for immediate learning against the long-term vision of scale, all without falling into the trap of premature platform building. You are a natural relationship-builder who earns trust quickly, operates with a growth mindset, and knows how to lead initiatives from the whiteboard to production.
Our ideal candidate meets the following requirements:
Experience: 6+ years of hands-on expertise building, scaling, and operating large scale data and ML infrastructure, including production-grade pipelines, feature stores, model-serving layers or end-to-end ML systems.
Foundational ML & AI Expertise : Deep understanding of Machine Learning fundamentals and the production systems they require — data flows, serving infrastructure, observability, and evaluation. Familiarity with agentic patterns including orchestration, tool use, skill-based architectures, memory, and retrieval strategies (embeddings, hybrid search, ranking)
Technical Stack: Proficiency with agentic AI frameworks (e.g., LangGraph, LangChain, Agents SDK), large-scale data processing (Spark, Databricks, Airflow), cloud platforms (AWS preferred), and vector stores. Python required.
Leadersh
Posted June 23, 2026