> Markdown version of [/jobs/ext/1282599-machine-learning-scientist-ii](https://www.wearedevelopers.com/jobs/ext/1282599-machine-learning-scientist-ii). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Scientist II

- **Company:** Expedia Inc.
- **Location:** San Jose, CA, United States
- **Experience:** Experienced
- **Salary:** $112,000.0 - $196,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Databases, Memory Management, Python (Programming Language), Machine Learning, Open Source Technology, Tensorflow, Pytorch, ReactJS, Large Language Models, Multi-Agent Systems, Prompt Engineering, Information Technology, Machine Learning Operations
- **Published:** July 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=3532957e52c02e61

## About the Role

* 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, * 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

## 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


## Related Videos

- [Watch Tests Go Brrrr! : Getting Started with Cypress in ReactJS](https://www.wearedevelopers.com/videos/282-watch-tests-go-brrrr-getting-started-with-cypress-in-reactjs)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases)
- [LLMs in the wild: Building an AI agent that survives production](https://www.wearedevelopers.com/videos/100319-llms-in-the-wild-building-an-ai-agent-that-survives-production)
- [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery)
- [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models)

## Related Articles

- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)