Machine Learning Scientist II - Agentic Experiences

Expedia Group•San Jose, CA

2d•$112,000 - $171,500

About The Position

Expedia Group is a global travel company that connects travelers, partners, and advertisers through its consumer brands, B2B network, and travel advertising business. The Traveler Discovery & Planning team is at the forefront of innovation in AI-driven agentic systems, aiming to enhance customer experiences, increase engagement, and strengthen traveler relationships through cutting-edge machine learning solutions. This role is part of the Agentic Experiences team, which is revolutionizing traveler experiences through autonomous, intelligent agent systems. The work involves building conversational AI that anticipates traveler needs and developing proactive solutions for personalized trip planning, shaping the future of travel assistance and impacting millions of users worldwide. This is an opportunity to build foundational systems in a high-impact domain, supported by Expedia’s AI-first vision.

Requirements

PhD, MS, or BS in Computer Science, Machine Learning, Statistics, Engineering, or a related field; or equivalent professional experience
2+ years of related industry experience
Demonstrated experience designing and deploying agentic or multi-step AI systems (e.g., ReAct, tool-calling agents, multi-agent pipelines) in production or research settings
Proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX); experience with LLM APIs and orchestration libraries (e.g., LangChain, LlamaIndex, or similar)
Experience integrating LLMs with external tools, APIs, and structured data sources for real-world task completion
Understanding of prompt engineering techniques including chain-of-thought, few-shot prompting, and structured output generation
Experience defining and running evaluation frameworks for ML systems, including offline benchmarking and production monitoring

Nice To Haves

Experience in the travel or e-commerce industry
Publications in top-tier ML conferences or journals
Patented Inventions, pending and issued
Contributions to open-source ML projects
Experience taking models from prototype to production in collaboration with Machine Learning Engineering teams

Responsibilities

Design, build, and evaluate multi-step agentic AI systems, including autonomous agents capable of planning, tool use, memory management, and multi-agent collaboration
Research and implement state-of-the-art techniques in agentic architectures, such as ReAct, reflection loops, chain-of-thought prompting, and tool-augmented reasoning
Develop and maintain agent orchestration frameworks, defining how agents decompose tasks, delegate to sub-agents, and handle failure and recovery
Integrate large language models (LLMs) with external tools, APIs, databases, and code execution environments to enable real-world task completion
Define and own evaluation frameworks for agentic systems, measuring task success, reliability, latency, cost, and safety across diverse benchmarks and production scenarios
Collaborate closely with product, engineering, and research teams to translate business requirements into agentic system designs and deliver production-grade solutions
Identify and mitigate risks specific to agentic systems, including prompt injection, unintended actions, hallucination in long-horizon tasks, and unsafe tool use
Stay current with the rapidly evolving agentic AI landscape, synthesizing academic research and industry developments to inform the team's technical direction