Senior Machine Learning Scientist, Agentic AI & GenAI Systems (Growth Marketing)

Expedia•Seattle, WA

About The Position

At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business. Here, you'll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors-Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together-help foster a supportive environment where people can grow their careers and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere. Sr. Machine Learning Scientist, Agentic AI & GenAI Systems (Growth Marketing) We create and deliver tailored marketing strategies for Expedia Group’s brands, focusing on establishing strong connections and cohesive experiences for travelers and partners. We leverage our functional expertise and creative excellence to build trust and loyalty for our brands through innovative marketing approaches and technology. Our Growth Marketing team is redefining how AI-driven, agentic automation meets data-powered marketing. We build production-ready multimodal LLMs, GenAI, and agentic architectures that power personalized travel discovery, real-time cross-platform campaign execution, and interactive chatbots/UIs. For example, “Trip Matching” on Instagram transforms inspiring reels and posts into instant hotel recommendations, powered entirely by our production AI. We are looking for a hands-on Senior Machine Learning Scientist who thrives on building end-to-end systems , from backend architecture to user-facing interfaces , including agentic UX/UI, observability, and product integration .

Requirements

10+ years in software engineering, ML, and AI systems, with production GenAI deployments.
Deep expertise in LLM training, adaptation, distillation, RLHF/DPO, and RAG systems.
Solid foundation in NLP and experience with multimodal AI systems (vision-language models)
Proven experience building and operating multi-agent AI platforms with observability and safety frameworks (self-hosted orchestration using frameworks such as LangGraph integrated with LLM APIs such as Claude).
Strong background in distributed GPU training and inference, cloud infrastructure (AWS/Azure), container orchestration, and ML tooling.
Demonstrated ability to lead end-to-end AI product development and collaborate with product and design teams to ship user-facing features.
Excellent communication skills, able to present complex architecture and product concepts to executives.

Nice To Haves

PhD in Computer Science, Machine Learning, or a related field.
Recognized industry presence through publications, patents, talks, or open-source contributions in LLMs, RAG, or agentic systems.
Experience integrating multimodal LLM systems (vision, audio, music, structured data).
Leadership in GenAI safety, evaluation, testing, and monitoring.
Strong cross-disciplinary fluency in modeling, infrastructure, product, and design.

Responsibilities

Architect, build, and ship enterprise-scale GenAI, RAG, and multi-agent systems end-to-end , including frontend, backend, and user interfaces.
Design hierarchical multi-agent ecosystems with Interactive Generative UIs, dashboards, and safety/observability features (e.g., UI is generated on-the-fly by an agent in response to what the user need ).
Develop memory architectures: short-term contextual memory, long-term episodic memory, knowledge graph augmentation, and adaptive retrieval systems.
Lead hands-on implementation of RAG pipelines, vector memory systems, and agent orchestration frameworks (LangChain, LangSmith, AutoGen, OpenAI/Claude Agents SDK, etc) and advanced evaluation platforms leveraging LLM-as-a-Judge, synthetic data generation, and agent trajectory assessment.
Train, fine-tune, adapt (LoRA/QLoRA/adapters), and distill LLMs, including RLHF/DPO, for production-ready chatbots and GenAI products.
Build multimodal pipelines integrating vision, audio, text, and structured data , optimizing for scalability and latency .
Build and deploy large-scale behavioral embedding systems that learn traveler representations from billions of search, booking, web, app, loyalty, and marketing interactions, leveraging distributed representation learning and real-time serving to power personalization, audience targeting, propensity models, and next-best-action recommendations.
Collaborate with product, and engineering teams to create intuitive, user-friendly interfaces and ensure seamless end-to-end user experiences.
Mentor engineers and ML scientists, set technical standards, conduct design reviews, and contribute to the organization’s technical maturity.
Represent the Org externally via open-source contributions, patents, conferences, and publications .