AI Engineer

• Design and implement scalable AI architectures, including: LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, agentic / multi-step workflows, vector search and retrieval services, model serving and inference layers.
• Establish reusable platform services, APIs, and design patterns to accelerate delivery across multiple teams.
• Build and operationalize AI delivery pipelines: CI/CD for models, prompts, and workflows, prompt versioning and lifecycle management, evaluation and testing frameworks, model and artifact registries.
• Implement monitoring for response quality and hallucination control, latency, throughput, and system reliability, cost observability and optimization.
• Design AI systems with strong controls for data security and privacy auditability and traceability, entitlements and access controls, data lineage and governance.
• Lead delivery of production-grade AI systems with a focus on scalability and reliability, latency and performance optimization, operational readiness and support.
• Partner closely with data engineering and platform teams to integrate AI capabilities with Snowflake and Databricks environments, structured and unstructured data pipelines, APIs and enterprise data services, semantic and knowledge-layer architectures.

Director, AI Engineering – Agentic Platform

Machine Learning Engineer

• Architect the ranking backbone for query understanding and personalization
• Design and build a search autosuggest system
• Develop production-grade Multi-Task Learning
• Own the inference layer for quality optimization
• Advance evaluation practices for long-horizon metrics
• Partner across teams to translate business goals into ranking policies
• Mentor ML engineers in ranking expertise

Senior Machine Learning Engineer II, Search & Recommendations Ranking

• Design and build multi-agent orchestration patterns (master-orchestrator + specialized worker agents) using protocols such as MCP and A2A
• Lead end-to-end delivery of agentic underwriting and claims automations, from prototype through production hardening
• Partner with IE on orchestration entry-point design (e.g., Azure Function endpoints, master-agent gateways) so the AE/IE seam is clean and scalable
• Lead offshore engineers and team members on agentic patterns, prompt engineering, and reliability practices
• Establish coding, evaluation, and observability standards for agentic systems within the AI & Automation Center of Excellence
• Translate business intent into working agentic systems — not just systems that compile, but systems that deliver measurable business outcomes.

AVP, AI Engineering

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.
• You will build models that automate refunds, getting money back to our customers faster.
• You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.
• You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
• You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.

Machine Learning Engineer II

• Own and drive the AI Platform roadmap and delivery
• Drive the development of core platform capabilities
• Build a modular, interoperable platform architecture
• Enable downstream product teams to build high-quality AI-driven experiences
• Establish a rigorous evaluation framework for platform performance
• Operate across multiple workstreams simultaneously
• Bring a strong point of view on agentic systems and enterprise AI patterns

Senior Platform Product Manager, AI Platform

• Set the technical direction for the agentic data foundations
• Design, prototype, and ship systems for agent interactions
• Lead complex, multi-team initiatives across Agentic AI and Platform teams
• Mentor Senior and Staff engineers

Principal Machine Learning Engineer

• Develop actionable insights and recommendations in support of business objectives using in-house and external data sources
• Collaborate with business and data engineering professionals for timely and reliable delivery of data solutions
• Leverage deep expertise in data engineering, data science, advanced analytics, and insurance industry data to aid business partners in risk management
• Drive efficiencies and improve customer experience
• Partner with business stakeholders to scope, structure, and drive analytical projects from requirements through delivery
• Present findings and recommendations clearly to both technical and non-technical audiences
• Source, validate, and integrate structured and unstructured data across platforms
• Conduct exploratory data analysis to reveal data quality issues and identify opportunities
• Analyze business trends, time series patterns, model performance, and causal relationships using advanced statistical and machine learning / deep learning methods
• Design, build, and deploy machine learning / deep learning models — including NLP, computer vision, and LLM-based solutions
• Create visualizations, dashboards, and reports that communicate analytical and model results to support decision-making across the organization
• Own and maintain recurring analytical, reporting, and model-monitoring processes
• Document data pipelines, model architecture, analytical frameworks, and deliverables for knowledge sharing across technical and non-technical teams

Machine Learning Scientist

• Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.
• Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.
• Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations.
• Accountable for deployment design, development and maintenance of both traditional ML and AI models.
• Work with junior engineers and peers to provide mentorship and thought leadership.
• Be comfortable presenting new concepts to technical audiences.
• Collaborate with partners Enterprise Data, Applied AI, Business, Cloud Enablement Team, and Enterprise Architecture teams
• Delivery of critical milestones for model deployment in the AWS and GCP clouds.
• Adopt and promote MLOps best practices to the Data Science community.

Senior AI Machine Learning Engineer

• Design, build, and maintain AI components and data pipelines that support applied AI use cases
• Contribute to the development and operation of batch and real-time data/AI pipelines supporting AI model training and inference
• Implement and maintain production AI assets in alignment with defined architecture, coding standards, and operational practices
• Collaborate closely with senior Applied AI Engineers, Data Engineers, Architects, Scientists to implement approved solution designs
• Support deployment, monitoring, and maintenance of AI services and workflows in production environments
• Apply established CI/CD, testing, and observability practices to ensure solution quality and reliability
• Assist in identifying, integrating, and validating data sources to ensure data quality and suitability for AI and analytics use cases
• Participate in code reviews, design discussions, and knowledge sharing to continue developing applied AI and data engineering expertise

AI/ML Engineer, EB Applied AI and Analytics

• Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.
• Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.
• Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations.
• Accountable for design, development and maintenance of Models as Service.
• Work with junior engineers and peers to provide mentorship and thought leadership.
• Be comfortable presenting new concepts to technical audiences.
• Collaborate with partners Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams.
• Delivery of critical milestones for model deployment in the AWS and GCP clouds.
• Adopt and promote MLOps best practices to the Data Science community.

Senior AI, Machine Learning Engineer

• Automate and orchestrate machine learning workflows across major cloud and AI platforms (AWS, Azure, Databricks, and foundation model APIs such as OpenAI)
• Maintain and version model registries and artifact stores to ensure reproducibility and governance
• Develop and manage CI/CD for ML, including automated data validation, model testing, and deployment
• Implement ML Engineering solutions using popular MLOps platforms such as AWS SageMaker, MLflow, Azure ML
• Scale end-end custom Sagemaker pipelines
• Design and implement the engineering components of GAR+RAG systems (e.g., query interpretation and reflection, chunking, embeddings, hybrid retrieval, semantic search), manage prompt libraries, guardrails and structured output for LLMs hosted on Bedrock/SageMaker or self-hosted
• Design and implement ML pipelines that utilize Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs
• Build evaluation pipelines: offline IR metrics (NDCG, MAP, MRR), LLM quality metrics (faithfulness, grounding), and A/B testing
• Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization
• Stay current with the latest GAI research, NLP and RAG and apply the state-of-the-art in our experiments and systems
• Partner with Subject-Matter Experts, Product Managers, Data Scientists and Responsible AI experts to translate business problems into cutting edge data science solutions
• Collaborate and interface with Operations Engineers who deploy and run production infrastructure.

Senior ML Ops Engineer

• Develop and implement advanced search solutions
• Lead the development and optimization of search algorithms and systems utilizing technologies such as Elastic/OpenSearch
• Fine-tune search relevance using machine learning techniques
• Explore and implement cutting-edge technologies including Agentic Solutions, RAG, and LLMs
• Analyze search data and user behavior to inform optimization strategies
• Collaborate with cross-functional teams to understand search requirements and deliver results
• Develop and evaluate analytical models to investigate search-related problems
• Utilize strong programming skills in Python to examine and interpret search data

Senior Software Development Engineer – Search Machine Learning

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• Lead the strategy, architecture, and enablement of the enterprise cloud and AI platform ecosystem
• Partner across product, security, data, and application teams to deliver secure, reliable, scalable cloud capabilities
• Set and communicate the vision, operating model, and multi-year roadmap for Multi cloud & AI Platforms
• Own the developer enablement strategy, including CI/CD patterns and self-service automation
• Define and evolve the enterprise AI platform architecture
• Partner with Security, Risk, and Compliance to embed security-by-design and data protection
• Drive cloud financial management (FinOps)
• Define platform standards and reference architectures and influence AI adoption
• Measure and improve platform outcomes using KPIs
• Represent the platform organization in executive forums

Senior Director, Cloud and AI Platforms

AI Research Scientist

• Conduct applied research in knowledge graphs, graph reasoning, and neuro-symbolic AI
• Prototype graph-enabled solutions for real business problems such as fraud detection and relationship discovery
• Evaluate architectures and reasoning strategies to inform platform and roadmap decisions
• Define and implement ontology and semantic modeling patterns
• Design entity resolution and linkage approaches
• Partner with engineering, platform, and MLOps teams to operationalize graph capabilities with enterprise-grade standards
• Design evaluation frameworks for reasoning quality, robustness, and traceability
• Contribute reusable patterns, documentation, and integration guidance

Senior Applied AI Scientist, Knowledge Graph

• Work on high-impact problems at the intersection of LLM research, large-scale ML systems, and real-world e-commerce applications.
• Choose to work in areas like query understanding, search relevance and ranking, generative recommendations, LLM evaluation, and more.

Machine Learning Engineer, PhD Intern

• Own strategy and evolution of platform capabilities that enable AI assistants, workflows, and integrations.
• Define how internal AI systems and external partners connect to FUB data through well-designed tools and integration patterns.
• Shape intelligence capabilities that help AI better understand agent and lead activity.
• Lead product direction for memory capabilities that make AI experiences more personalized over time.
• Partner closely with engineering, AI/ML, design, and product peers to assess tradeoffs and define solutions.
• Help identify and prioritize highest-value platform opportunities based on product needs and long-term strategy.
• Drive alignment across teams by communicating roadmap priorities and progress clearly to stakeholders and leadership.
• Contribute to long-term vision for how AI platform capabilities show up in product experiences across web, mobile, automation, and assistant surfaces.

Senior Product Manager, AI Platform – Ecosystem

• Lead the design, prototyping, and deployment of scalable machine learning infrastructure that can power Zillow AI Mode.
• Serve as a critical technical liaison between the Agentic Data Platform and applied AI teams.
• Collaborate closely with scientists, engineers, and product partners to understand their dependencies and deliver the data foundations they need to innovate rapidly.
• Drive alignment on timelines, architectures, and prioritization across different organizations, influencing technical direction without requiring direct authority.
• Deploy and optimize capabilities in production environments, ensuring high availability, low latency, and efficient resource utilization capable of handling massive scale.
• Mentor other engineers, guiding them in using the right technologies and establishing best practices.
• Translate complex ML infrastructure designs and ambiguous customer problems into clear, actionable insights for diverse audiences, including leadership and non-technical stakeholders.

Senior Machine Learning Engineer

• Play a key role in building the internal platform which supports training and deploying AI models across the entire organization.
• Take ownership of defining the platform to enable AI model fine-tuning and batch inference by building the SDKs and supporting the infra.
• Manage cross-cutting stakeholder relationships, prioritizing customer needs first.
• Navigate ambiguous, hairy, and technical problem spaces.
• Jump into domains, languages, and problem areas that might be new and unfamiliar.

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