Research Engineer, Retrieval & Search
@ OpenAI

San Francisco, CA
$200,000
On Site
Full Time
Posted 12 hours ago

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Job Details

About The Team

We bring OpenAI's technology to the world through products like ChatGPT and the OpenAI API. OpenAI is dedicated to ensuring that AI benefits all of humanity while prioritizing safety over unfettered growth.

About The Role

As a Research Engineer, Retrieval & Search, you will work on retrieval and search problems across our API and ChatGPT. You will drive initiatives on document search, enterprise search, knowledge retrieval, and web-scale search, impacting millions of end users.

  • Collaborate with research teams to develop novel search algorithms.
  • Deploy production-grade search methodologies for API and ChatGPT.
  • Explore and research retrieval topics influencing product strategy.
  • Partner with cross-functional teams to bring new features to life.

You Might Thrive In This Role If You

You bring extensive experience in building and maintaining production machine learning systems, have worked with vector databases or search indices, and can own problems end-to-end in an evolving environment.

About OpenAI

OpenAI is an AI research and deployment company that pushes the boundaries of appropriate and safe AI technology deployment. The company emphasizes responsibility, human-centric safety, and diverse perspectives.

Key skills/competency

  • Machine Learning
  • Search Algorithms
  • Retrieval
  • Production Deployment
  • Vector Databases
  • Document Search
  • Enterprise Search
  • Web-scale Search
  • Collaboration
  • Research

How to Get Hired at OpenAI

🎯 Tips for Getting Hired

  • Research OpenAI's culture: Understand OpenAI's mission, values, and recent developments.
  • Customize your resume: Highlight production machine learning and search system experience.
  • Showcase technical skills: Emphasize retrieval, vector databases, and deployment projects.
  • Prepare for interviews: Review algorithm design and problem-solving scenarios.

📝 Interview Preparation Advice

Technical Preparation

Review search algorithm design.
Study vector database integration.
Practice production deployment scenarios.
Refresh machine learning system architecture.

Behavioral Questions

Describe cross-team collaboration experience.
Share problem-solving in ambiguous projects.
Explain handling multiple priorities.
Discuss learning new technical skills.

Frequently Asked Questions