Senior Software Engineer, Applied AI Services

About the role

Shopper Journey Services (SJS) is Zillow's engine for authenticated user intent and a core enabler of Home Details Page (HDP) experiences. We gather rich, explicit and implicit signals from authenticated shoppers and co‑shoppers, refine them into durable models, services, and display‑ready data, and activate those capabilities across Zillow's ecosystem to deliver personalized, high‑quality experiences. Our charter spans two tightly connected areas: User Intent & Applied AI, where we build the pipelines, services, and evaluation capabilities that turn user signals into intelligence and AI‑powered experiences, and the HDP Backend Platform, where we power HDP with robust, low‑latency, display‑ready data. AI is both a product capability and an engineering accelerator for this team, and we are investing in LLM‑ and ML‑powered workflows, evaluation, and agentic systems while modernizing our conventional backend stack. We are looking for a Senior Software Engineer, Applied AI Services to build and scale end‑to‑end intelligent systems that power personalized experiences and HDP‑aligned capabilities. This is a hands‑on IC role where you'll spend most of your time writing code and driving technical design for impactful production systems. You will operate at the intersection of: Scalable backend services (APIs, events, data stores, cloud infrastructure), and Applied AI/ML systems (offline data ingestion, feature and signal pipelines, LLM/ML‑powered capabilities, evaluation frameworks, and AI‑driven workflows). You will help turn user intent into intelligence, safely bring new AI capabilities from 0→1, and scale them across Zillow's shopper journey surfaces. You Will Get To Lead end‑to‑end delivery of features - from early prototypes to production‑hardened systems. Work deeply with AI/ML workflows while applying solid engineering and reliability practices. Design, build, and maintain data pipelines (e.g., Spark, Databricks, Python, Kafka or equivalents) that turn raw events and signals into durable features and model inputs. Develop and integrate ML/LLM capabilities (e.g., embeddings, similarity search, ranking, clustering, text generation) into backend flows and expose them via stable, well‑versioned services and APIs for product teams to consume. Design and iterate on prompts, configurations, and AI workflows using notebooks and offline experimentation, and build automated evaluation systems (e.g., LLM‑as‑judge, regression suites, sampling pipelines) with clear quality metrics for accuracy, safety, latency, and cost. Translate prototypes into reliable, observable production systems with solid tests, deployment pipelines, and on‑call readiness; own their operational health through runbooks, incident reviews, and continuous improvements, while optimizing for performance, reliability, and cost. Collaborate closely with AI/ML and Agentic AI teams to bring 0→1 AI capabilities to customers and evolve them to 1→N, partner with SJS and HDP engineers on clean interfaces between offline pipelines, model outputs, and online APIs/events, and contribute reusable libraries, templates, and examples that help other engineers ship AI‑powered features faster. This role has been categorized as a teleworker position. Teleworkers 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. Employees may live anywhere in Mexico, with availability to travel to Mexico City, as we recommend attendance at occasional office events. In addition to a competitive base salary and benefits, this position is also eligible for equity awards based on factors such as experience, performance and location.

Requirements

• You are strongly proficient in at least one backend programming language such as Python, Java, Kotlin, or similar, and have delivered scalable services or APIs to production in a cloud environment (e.g., AWS, GCP), including thoughtful data modeling and performance tuning.
• You have hands‑on experience with data processing or distributed systems (e.g., Spark, Databricks, Kafka or similar data pipelines) and are comfortable designing and consuming APIs (REST or GraphQL).
• You've worked with ML or LLM‑based systems and shipped at least one feature or capability to production, in partnership with an AI/ML team or self‑built.
• You're familiar with embeddings, ranking, clustering, recommendations, or other ML applications that support personalization and search/browse experiences.
• You have experience with prompt engineering and LLM‑based product development, including safety/guardrail considerations and offline/online evaluation frameworks for AI‑powered features.
• You are comfortable in modern infrastructure environments (e.g., Kubernetes‑based deployments, event‑driven architectures, and observability stacks) and can translate prototypes into robust, observable, well‑tested production systems.
• You have a track

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