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

Expedia Group•Seattle, WA

8d•$173,000 - $242,500

About The Position

Expedia Group’s Growth Marketing team develops tailored marketing strategies and technology to build cohesive experiences and strong connections with travelers and partners. The team is building production-ready multimodal LLMs, GenAI, and agentic architectures for personalized travel discovery, cross-platform campaign execution, and interactive chatbots and interfaces. Examples include Trip Matching on Instagram, which turns travel inspiration into hotel recommendations. The Senior Machine Learning Scientist will build end-to-end AI systems, spanning backend architecture, user-facing interfaces, agentic UX/UI, observability, and product integration. The role includes developing enterprise-scale GenAI, RAG, multi-agent and multimodal systems; building memory and retrieval architectures; adapting and training LLMs; and creating large-scale behavioral embedding systems for personalization and marketing. The scientist will work across product and engineering, mentor colleagues, establish technical standards, and contribute to Expedia Group’s technical maturity and external presence.

Requirements

10+ years of experience in software engineering, machine learning, and AI systems, including 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, including vision-language models.
Experience building and operating multi-agent AI platforms with observability and safety frameworks.
Strong background in distributed GPU training and inference, cloud infrastructure such as AWS or 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, including the ability 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 involving vision, audio, music, or 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 and observability features.
Develop short-term contextual and long-term episodic memory architectures, knowledge graph augmentation, and adaptive retrieval systems.
Implement RAG pipelines, vector memory systems, agent orchestration frameworks, and evaluation platforms using LLM-as-a-Judge, synthetic data generation, and agent trajectory assessment.
Train, fine-tune, adapt, and distill LLMs, including use of LoRA/QLoRA/adapters and RLHF/DPO, for production 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 from traveler interactions to support personalization, audience targeting, propensity models, and next-best-action recommendations.
Collaborate with product and engineering teams to create intuitive interfaces and seamless end-to-end user experiences.
Mentor engineers and ML scientists, set technical standards, conduct design reviews, and contribute to organizational technical maturity.
Represent the organization externally through open-source contributions, patents, conferences, and publications.