Principal Machine Learning Engineer

About the team

Zillow is building the next generation of AI experiences for the millions of customers who are navigating one of the most important decisions of their lives. Agentic Data Platform's(ADP) mission is to power Zillow's agentic future by exploring and delivering bleeding-edge foundational platform capabilities that make agentic systems scalable across Zillow.

Agentic Data Platform(ADP) operates as a small, high-leverage 'startup-within-the-company' to bridge Zillow's broader platform and the Agentic AI organization that ships customer-facing agentic experiences at scale. We are looking for a Principal engineer with strong, senior-level leadership - someone who sets strategic and technical direction for ADP, and partners deeply across Agentic AI and the broader platform orgs.

About the role

As a Principal Machine Learning Engineer in the Agentic Data Platform organization, you will:

• Set the technical direction. Define and own the multi-quarter architecture roadmap for the agentic data foundations (Context engineering, Agentic memory, and AI workflows) that power Zillow's agentic experiences.

• Architect and ship at scale. Design, prototype, and ship systems that handle hundreds of millions of agent interactions with high availability, low latency, and predictable cost. Stay hands-on in code and production when it matters.

• Drive cross-organization execution. Lead complex, multi-team initiatives across Agentic AI and Platform teams - aligning on architecture, surfacing dependencies, and driving outcomes through influence rather than direct authority.

• Communicate to every level. Translate complex platform trade-offs, ambiguous customer problems, and emerging agentic paradigms into clear, actionable insights for engineering peers, product partners, Directors, and VPs

• Grow senior technical talent. Mentor Senior and Staff engineers, raise the bar on technical judgment and architecture decisions, and shape the engineering culture of the org.

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 $204,400.00 - $326,600.00 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 $194,200.00 - $310,200.00 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've shipped agentic systems in production and you've built large-scale platform infrastructure and you know the failure modes specific to doing both at once, what's worth abstracting, and what isn't yet. You move comfortably between architecting a multi-quarter platform investment and writing the prototype that proves it works.

You resist premature platform building: you ship the smallest foundation that meets a real need, then harden the pattern once it's clear. You think in production grade defaults — observability, evaluation, safety, latency, cost — and you raise the bar quietly through the systems and docs you leave behind. You operate well in ambiguity, earn alignment across science, engineering, and product through clear writing and sharp design, and lead from the front: whiteboard, design doc, production code.

Our ideal candidate meets the following requirements

• Experience. 10+ years building, scaling, and operating large-scale data and ML infrastructure (production-grade pipelines, feature stores, and model-serving layers), with 1 to 2 of those recent years shipping agent-based or LLM-powered systems to production. 3+ years as a technical leader spanning multiple organizations.

• Agentic systems expertise. Hands-on ex