OpenAI
Research Engineer / Research Scientist - Personal AGI, Personality and Model Behavior
About this role
OpenAI seeks a Research Engineer/Scientist to advance personalization and behavioral modeling for AGI systems. You'll develop reinforcement learning and reward modeling techniques to create individually tailored model behaviors, integrating research into ChatGPT and other products used by hundreds of millions.
What you'll do
- Conduct research on model personalization, personality, and behavior using synthetic data, reward modeling, and reinforcement learning
- Design and build robust evaluation frameworks and training pipelines for personality research
- Innovate post-training methods to shape model behavior and emotional intelligence
- Debug and optimize large ML codebases to implement research findings
- Collaborate with product teams to integrate research into deployed systems
- Develop tools and methodologies for understanding user preferences in model behavior
What they're looking for
- Machine learning and deep learning fundamentals
- Reinforcement learning and reward modeling
- Model training and optimization
- Evaluation design and metrics
- Large codebase debugging and navigation
- Post-training techniques (RLHF, fine-tuning)
- Python and ML frameworks
- Product-driven research mindset
Benefits
- Hybrid work model (3 days/week in San Francisco office)
- Relocation assistance for new employees
- Work on cutting-edge AGI systems used by hundreds of millions
- Collaborate with top AI researchers at OpenAI
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OpenAI
OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.
View all jobs at OpenAILikely interview questions
- Describe a time you debugged a complex issue in a large ML codebase—what was your approach?
- How would you design an evaluation framework to measure whether a model's personality aligns with individual user preferences?