Research Engineer / Research Scientist – Personal AGI, Proactivity
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.
AI @ 3
ChatGPT
LLM @ 3
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 Proactivity Research team, within OpenAI’s broader Personal AGI team, is focused on making models in ChatGPT and future potential products proactive in ways that are truly useful. The team is laying the technical foundations for AI that can anticipate what users need in real time, adapt as their goals and preferences shift, and build a deeper, evolving understanding of the person it is helping.
About the Role
Research and develop improvements to model personalization and agentic capabilities. The team works on reinforcement learning, dataset creation, evaluations, and other post-training methods, partnering closely with research and product teams across the company to develop a highly personalized, collaborative, and proactive assistant.
The role requires strong machine learning engineering skills and research experience, especially with novel and highly capable models. Product-driven research experience or interest is valued.
Responsibilities
- Own and pursue a research agenda to improve model proactivity and the ability of models to further user goals.
- 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
- Deep understanding of machine learning and machine learning applications.
- Working knowledge of LLM post-training and evaluation approaches.
- Passion for, or experience thinking about, personalization and enabling users to achieve their goals.
- Ability to work in a large machine learning codebase and debug effectively.
- Ability to thrive in a dynamic and technically complex environment.
Work Arrangement
This role is based in San Francisco, California, and follows a hybrid work model requiring three days in the office per week.
Benefits
- Equity, performance-related bonuses for eligible employees, and a base salary range of $295,000–$555,000 per year.
- Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
- Pre-tax accounts for Health FSA, Dependent Care FSA, and 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 applicable 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.