Senior Machine Learning Engineer

About the team

Zillow is seeking a highly experienced, product-minded Senior Machine Learning Engineer to anchor our Agentic Data Foundations team within the broader Agentic Data Platform organization. In this role, you will be the bridge between our core data platforms and the AI teams building Zillow’s next-generation customer experiences.

You will design and build the highly scalable, robust foundational infrastructure that empowers our AI teams. We need a "roll-up-your-sleeves" technical leader who thrives in ambiguity, holds a high bar for production engineering, and possesses the ability to align multiple teams around a shared technical vision.

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.

This role has been categorized as a Remote position. “Remote” employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice, which must be identified to the Company. U.S. employees may live in any of the 50 United States, with limited exceptions.

In California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $171,700.00 - $274,300.00 annually. This base pay range is specific to these locations and may not be applicable to other locations. In Colorado, Hawaii, Illinois, Minnesota, Nevada, Ohio, Rhode Island, and Vermont the standard base pay range for this role is $163,200.00 - $260,600.00 annually. The base pay range is specific to these locations and may not be applicable to other locations.

In addition to a competitive base salary this position is also eligible for equity awards based on factors such as experience, performance and location. Actual amounts will vary depending on experience, performance and location. Employees in this role will not be paid below the salary threshold for exempt employees in the state where they reside.

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 (em