Machine Learning Scientist II - Agentic Experiences

Expedia Group•San Jose, CA

1d•$112,000 - $180,500

About The Position

The Machine Learning Scientist II will join Expedia Group’s Traveler Discovery & Planning team, which develops AI-driven agentic systems to improve customer experiences, engagement, and traveler relationships. The role focuses on building foundational systems for intelligent travel assistance, including conversational AI and proactive personalized trip-planning solutions that can affect travelers worldwide. The scientist will design, implement, and evaluate multi-step agentic AI systems; research agent architectures and reasoning techniques; develop orchestration frameworks; and connect large language models to tools and data sources so they can complete real-world tasks. The role also owns evaluation approaches for system performance, reliability, safety, latency, and cost, and works with product, engineering, and research teams to bring production-grade solutions to users. The position requires identifying and mitigating risks such as prompt injection, unsafe tool use, unintended actions, and hallucinations in long-running tasks. The position is based in Seattle, Austin, or San Jose. The listed total cash ranges vary by location: $112,000–$156,500 in Seattle, $119,000–$166,500 in Austin, and $129,000–$180,500 in San Jose. The posting also states that sustained performance may raise pay to the top of the respective range: $179,000, $190,500, or $206,500.

Requirements

PhD, MS, or BS in Computer Science, Machine Learning, Statistics, Engineering, or a related field, or equivalent professional experience.
At least 2 years of related industry experience.
Demonstrated experience designing and deploying agentic or multi-step AI systems, such as ReAct, tool-calling agents, or multi-agent pipelines, in production or research settings.
Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX.
Experience with LLM APIs and orchestration libraries such as 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 ML-system evaluation frameworks, 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 or 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 agentic architecture techniques, including ReAct, reflection loops, chain-of-thought prompting, and tool-augmented reasoning.
Develop and maintain agent orchestration frameworks, including task decomposition, delegation to sub-agents, failure handling, and recovery.
Integrate large language models with external tools, APIs, databases, and code execution environments to enable real-world task completion.
Define and own evaluation frameworks that measure task success, reliability, latency, cost, and safety across benchmarks and production scenarios.
Collaborate with product, engineering, and research teams to translate business requirements into agentic system designs and production-grade solutions.
Identify and mitigate agentic-system risks, including prompt injection, unintended actions, hallucinations in long-horizon tasks, and unsafe tool use.
Stay current with agentic AI research and industry developments, and synthesize findings to inform the team's technical direction.