Agentic AI, Senior Software Development Engineer

The Agentic AI team at Zillow is transforming the real estate industry by helping millions of people use AI assistants to find their next home. We are building always-on AI experiences that combine personalized user insights with Zillow’s deep real estate knowledge. The Agentic Evals team focuses on one of the hardest parts of building AI experiences: measuring quality. We build evaluation frameworks, tracing systems, observability tools, and feedback loops that help teams understand what works, identify failure modes, and continuously improve AI assistant behavior. Our work ensures Zillow’s AI agents remain trustworthy, responsible, measurable, and ready to scale. As part of this lean, customer-focused team, you will partner with applied scientists, software engineers, machine learning engineers, and product leaders to evolve Zillow’s next-generation AI platform. We are seeking a collaborative, product-minded backend engineer with strong Agentic AI fundamentals and a passion for building reliable, scalable systems that power AI applications.

Responsibilities

Design, build, and scale evaluation frameworks for Zillow’s agentic AI experiences; Build tracing, observability, and quality measurement systems for production AI agents; Create platform infrastructure that enables domain teams across Zillow to build on top of the Evals platform without bottlenecks; Partner with applied scientists and machine learning engineers to integrate new AI evaluation capabilities into production systems; Help evolve how Zillow measures AI quality, reliability, trustworthiness, and product impact; Stay current with emerging agentic AI paradigms, evaluation techniques, and LLM tooling, and translate them into practical platform innovation; Support scaling, reliability, performance optimization, incident response, and cost management for the evaluation layer; Apply first-principles thinking to ambiguous problems and iterate quickly on novel solutions

Requirements

4+ years of backend engineering experience, with a track record of designing, shipping, and operating scalable production ML services; Experience building platform infrastructure or developer-facing services consumed by multiple teams; you've thought carefully about APIs, user experience, reliability, and what it means to have internal customers; Hands-on experience with ML Evals & Observability frameworks (Databricks MLflow, or evolving LLM frameworks preferred - LangSmith, Braintrust, Promptfoo, or similar); Experience evaluating third-party AI platforms and making principled build vs. buy decisions for platform infrastructure; Fluency working across applied science, ML, product, design, and engineering; you translate between disciplines without losing precision; Comfort with LLMs, agentic systems, and evaluation frameworks; familiarity with orchestration tools (LangChain, LangGraph, MCP, or similar); A habit of reaching for AI-assisted development tools in your own workflow; you don't just build for AI, you build with it; Strong ownership and a growth mindset; you're energized by ambiguity, not slowed by it

Nice to have

Advanced degree in Computer Science, Machine Learning, or a related field, or equivalent practical experience building products using frontier LLMs, multimodal models, or agent-based systems; Experience designing and operating evaluation pipelines: offline batch evals, online scoring, cost/latency/signal tradeoffs, and closing the loop from user signals back to training data

Benefits

Equity awards