Research Engineer / Research Scientist - Personal AGI, Personalization
Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
ChatGPT
Machine Learning @ 6
Reinforcement Learning
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
About the Team
The Personalization-Memory team, within OpenAI's broader Personal AGI organization, develops agents that learn from prior interactions to become more helpful and efficient over time. The team builds general-purpose memory and personalization capabilities that transfer across ChatGPT and other agentic products, and collaborates with applied engineering on product surfaces that allow users to interact with memory.
About the Role
Research and develop improvements to memory usage and personalization in OpenAI's frontier models. The team works on reinforcement learning, dataset creation, evaluations, and other post-training methods, partnering closely with research and product teams to realize the vision of a personalized ChatGPT.
The role is suited to individuals with a background in frontier model post-training who can iterate quickly and are passionate about product-driven research.
Responsibilities
- 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 the research stack.
- Collaborate closely with research and product teams to influence the shape of technical solutions in the product.
Requirements
- Passion for personalization and building personalized assistants.
- Experience working with user signals and human data to turn feedback into reliable signals for training and evaluation.
- Deep understanding of frontier model post-training and machine learning applications.
- Commitment to principled approaches and research craftsmanship.
- Comfort diving into a large machine learning codebase to debug.
- Ability to thrive in a fast-paced, dynamic, and technically complex environment.
Work Arrangement
This role is based in San Francisco, California. The team uses a hybrid work model requiring three days in the office per week.
Benefits
- Equity, performance-related bonus opportunities for eligible employees, and competitive benefits.
- Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
- Pre-tax accounts for health and dependent care expenses, as well as commuter expenses.
- 401(k) retirement plan with employer match.
- Paid parental, medical, and caregiver leave.
- Paid time off, paid company holidays, office closures, and paid sick or safe time as required by law.
- Mental health and wellness support.
- Employer-paid basic life and disability coverage.
- Annual learning and development stipend.
- Daily office meals and eligible meal delivery credits.
- Relocation support for eligible employees.
- Additional benefits may include charitable donation matching and wellness stipends.