• Own the models and algorithms at the heart of Routing & Presence • Plan and scope ML work with observability and iterability designed in from the start • Propose and evaluate alternatives • Design experimentation frameworks tailored to routing outcomes • Lead innovation sessions with Product ahead of the product development cycle • Identify new data sources needed for learning and evaluation • Combine multiple signals into dynamic routing decisions • Shape the boundary between classical ML and LLM-driven components • Anticipate issues across the full lifecycle • Work across multiple teams and systems • Mentor scientists and engineers • Look ahead 18+ months • Build reputation as a subject-matter expert

• Work with large scale structured and unstructured data; explore, experiment, build and continuously improve foundation models for Airbnb product, business and operational use cases. • Create a multi-year tech roadmap that enables our team to stay on the leading edge of the rapidly evolving AI landscape and leverage the best in class technologies to deliver customer benefits. • Continuously evaluate recent and upcoming large foundational models, ensuring the selection and refinement of the highest quality models for enhanced performance and efficiency. • Hands-on prototype, develop and productionize LLM models and pipelines at scale, including both batch and real-time use cases. • Drive key AI architectural decisions for products, and contribute to Airbnb’s ML platform architecture and strategy.

• Build the evaluation and validation framework for all agent-driven clinical recommendations • Develop patient risk stratification models for adherence prediction, adverse event likelihood, dosage titration optimization, and churn/dropout risk • Implement and manage the predictive analytics pipeline on AWS • Design and build the RAG architecture that grounds agent responses in clinical protocols • Own model lifecycle management • Build explainability layers for clinical recommendations • Collaborate with the clinical team to translate clinical protocols and pharmacy domain knowledge into model features, training labels, and validation criteria • Establish model monitoring and alerting

• Optimize, standardize and implement data science and machine learning solutions at scale and in cloud-based environments (Azure) • Participate in the end-to-end lifecycle of data science projects through the use of DevOps, code, experiment and model management, CI/CD and further industry best practices • Write well-designed, testable, efficient code • Work closely with data engineering to continuously improve the way we consume data and deploy models in production • Design and lead on monitoring, troubleshooting, debugging and incident management for our ML pipelines • Be a trusted advisor and evangelist to the team and stakeholders on various aspects of ML Ops, from scaling and throughput, to infrastructure and deployment strategies