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Research Engineer Human-Centered AI

OpenAI

San Francisco, CAOn Site

Original Job Summary

About The Team

OpenAI is dedicated to ensuring our AI systems are safe, trustworthy, and aligned with human values. The team researches methods that guide AI in following human intent in diverse, high-stakes scenarios. A key focus is on leveraging high-quality human and synthetic data to train and evaluate models, ensuring effective alignment as capabilities grow.

About The Role

As a Research Engineer Human-Centered AI, you will research and model mechanisms that create value by explaining or predicting preferences and behaviors. You will quantify human behavior, design advanced labeling tasks, analyze user feedback, develop robust evaluations for alignment and real-world utility, and design new Human-AI interaction paradigms.

You Might Thrive In This Role If You

  • Have strong ML engineering skills and experience with frameworks like PyTorch.
  • Can bridge high-level research with low-level implementation details.
  • Are goal-oriented and comfortable with high-value, detailed work.
  • Have background in cognitive science, computational linguistics, or related fields.
  • Align with OpenAI’s mission of safe, beneficial AGI.

About OpenAI

OpenAI is an AI research and deployment company focused on ensuring that artificial intelligence benefits all of humanity. We push AI boundaries while keeping safety and human needs at the core, drawing on diverse perspectives.

Key skills/competency

  • ML Engineering
  • Human-AI Interaction
  • Research
  • Alignment
  • PyTorch
  • Data Analysis
  • Cognitive Science
  • Computational Linguistics
  • Evaluation
  • Fast-paced

How to Get Hired at OpenAI

🎯 Tips for Getting Hired

  • Customize your resume: Highlight ML and research experience.
  • Showcase projects: Emphasize human-AI interaction work.
  • Network smartly: Connect on LinkedIn and relevant forums.
  • Prepare for interviews: Review technical and behavioral insights.

📝 Interview Preparation Advice

Technical Preparation

Review PyTorch framework concepts.
Study scalable model evaluation techniques.
Practice coding algorithms for data analysis.
Familiarize with human-AI interaction research.

Behavioral Questions

Describe teamwork in challenging projects.
Explain decision-making under ambiguous situations.
Share experience managing detailed tasks.
Discuss adapting research to practical implementation.