• Architect, design, and implement Airbnb's next-generation big data compute platform to support data ETL, analytics, and machine learning initiatives. • Manage and operate the platform, continually enhancing its reliability, performance, observability, and cost-efficiency. • Produce high-quality, maintainable, and self-documenting code, actively participating in code review processes. • Contribute to open-source software and drive meaningful industry impact.

• Be hands-on (code, design, test) and collaborate with cross team partners (internal customers, dependencies and leadership) to deliver on multi-month projects in a timely fashion. • Design frameworks and maintain the general ecosystem around our NewSql database’s monitoring, permissions, service discovery integration, etc. • Empower and simplify the way engineers at Airbnb define and manage data by building and operating a generalized data schematization solution for a wide variety of storage backends at Airbnb and be able to satisfy broad compliance requirements.

• Collaborate with the Reliability Experience, Incident Management, Observability, and Resiliency teams to design and develop high quality UX. • Be an active contributor by creating high quality, tested pull requests and reviewing other’s designs and code. • Build appropriate tests to ensure reliability and performance of the software you create. • Create and present your own design, product, and architecture documents and provide feedback on others. • Stay up-to-date with the latest industry trends, technologies, and best practices in Web development and performance engineering.

• Lead the technical design and implementation of LLM-powered features for OneChat and enterprise AI tools, including RAG pipelines, agent orchestration, and prompt optimization • Partner with product managers, designers, and cross-functional teams to translate user problems into AI-powered solutions that serve Airbnb's global workforce • Architect and build production-ready AI/ML-integrated systems, ensuring scalability, reliability, and low latency across multi-cloud environments • Develop and iterate on agentic AI capabilities, including multi-step reasoning, tool use, and context-aware decision-making • Implement evaluation pipelines and quality systems to measure model performance, safety, and user satisfaction • Own production AI systems end-to-end, including deployment strategies, monitoring, alerting, and incident response • Collaborate with the DevAI team on AirChat SDK integrations, MCP (Model Context Protocol) implementations, and Glean Action Packs • Mentor engineers (L6-L8) through design reviews, architecture discussions, and pair programming sessions • Stay current with emerging AI technologies and evaluate their applicability to employee experience products • Balance hands-on technical contributions with technical leadership activities