Research Engineer / Research Scientist - Personal AGI, Personalization
About the Team
The Personalization-Memory team, within OpenAI's broader Personal AGI organization, is focused on developing agents that can learn from prior interactions in order to become more helpful and efficient over time. We build general-purpose memory and personalization capabilities that transfer across ChatGPT and other agentic products, and we collaborate with applied engineering on the product surfaces that allow users to interact with memory.
About the Role
As a Research Engineer / Research Scientist on the Personalization-Memory team, you will research and develop improvements to memory usage and personalization in OpenAI's frontier models. Our team works on reinforcement learning, dataset creation, evaluations, and other post-training methods. We partner closely with research and product teams across the company to realize the vision of a truly personalized ChatGPT.
We're looking for individuals who have a background in frontier model post-training, are able to iterate quickly, and who are passionate about product-driven research.
This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
In this role, you will:
- Own and pursue a research agenda for improving memory use and personalization in frontier models.
- Build robust evaluations for tracking modeling improvements.
- Design, implement, test, and debug code across our research stack.
- Collaborate closely with the research and product teams to influence the shape of technical solutions in the product.
You might thrive in this role if you:
- Are passionate about personalization and building personalized assistants.
- Have experience working with user signals and human data to turn feedback into reliable signals for training and evaluation.
- Have a deep understanding of frontier model post-training and machine learning applications.
- Value principled approaches and research craftsmanship.
- Are comfortable diving into a large ML codebase to debug.
- Thrive in a fast-paced, dynamic, and technically complex environment.