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# Machine Learning Scientist

- **Company:** Expedia Inc.
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Salary:** $187,000.0 - $261,500.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Databases, Software Design Patterns, Memory Management, Python (Programming Language), Machine Learning, Language Modeling, Open Source Technology, Tensorflow, Requirements Management, Software Engineering, Extensible Markup Language (XML), Scripting, Pytorch, ReactJS, Large Language Models, Multi-Agent Systems, Prompt Engineering, Information Technology, Machine Learning Operations
- **Published:** September 24, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-machine-learning-scientist-agentic-experience-san-jose-ca--e7a3debb-ec8f-462a-9b0d-11266a664452

## About the Role

* 8+ 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
 * Strong 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
 * Solid 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

Preferred Qualifications:

 * PhD, MS, or BS in Computer Science, Machine Learning, Statistics, Engineering, or a related field; or equivalent professional experience
 * 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, Academic Research, Advertising, Application Programming Interface (API), Artificial Intelligence (AI), Benchmarking, Best Practices, Budgeting, Business-to-Business (B2B), Computer Science, Consumer Branding, Customer Experience, Design Patterns Programming Methodologies, Injections, JAX (Java API for XML), Machine Learning, Memory Management, Mentoring, Modeling Languages, Open Source, Product Engineering, Production Control, Prototyping, Python Programming/Scripting Language, Requirements Management, Risk Analysis, Risk Management, Statistics, Structured Data, Technical Leadership, Travel Industry, Travel Planning, Willing to Travel, eCommerce

## Description

* 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
 * Mentor junior ML engineers and scientists, providing technical guidance on agentic design patterns, LLM best practices, and experimentation methodology


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