OpenAI
Research Engineer/Research Scientist, Personal AGI-Model Experience
About this role
Research Engineer/Scientist role at OpenAI focusing on improving model behavior and user experience for ChatGPT. You'll conduct research in reinforcement learning and model post-training, designing evaluations and implementing improvements to shape how the model communicates and behaves across different contexts.
What you'll do
- Own and execute a research agenda to enhance model capability and performance
- Build robust evaluation frameworks for tracking and measuring model improvements
- Design, implement, test, and debug code across the research infrastructure
- Collaborate with research and product teams to optimize model behavior
- Turn qualitative problems about model behavior into concrete training interventions
- Partner across OpenAI teams to ensure models are safe and reliable for millions of users
What they're looking for
- Machine learning and deep learning expertise
- Reinforcement learning
- Python and ML engineering
- Model evaluation and benchmark design
- Debugging large codebases
- Research communication and judgment
- LLM/transformer model understanding
- Post-training and fine-tuning techniques
Benefits
- Hybrid work model (3 days in office per week)
- San Francisco-based with relocation assistance
- Work on cutting-edge AGI research
- Collaboration with world-class AI research team
- Impact on millions of ChatGPT users
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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 your experience with reinforcement learning and how you've applied it to improve model behavior in past projects.
- Walk us through how you would design an evaluation framework to measure improvements in model communication quality.