Research Engineer / Research Scientist – Personal AGI (Post-Training)

at OpenAI

📍 San Francisco, United States

USD 295,000-555,000 per year

MIDDLE

✅ Hybrid

✅ Relocation

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.

API ChatGPT Machine Learning @ 6 Reinforcement Learning

Details

The Personal AGI team trains and improves pre-trained models for deployment into ChatGPT, the API, and future products. The team conducts research in partnership with research and product teams to prepare models for real-world deployment, ensuring they are safe, efficient, and reliable.

The role focuses on researching and developing model improvements in areas combining reinforcement learning and products. The successful candidate will have strong machine learning engineering skills, research experience with novel and highly capable models, and a passion for product-driven research.

This role is based in San Francisco, California, with a hybrid work model requiring three days in the office per week.

Responsibilities

Own and pursue a research agenda to improve model capability and performance.
Collaborate closely with research and product teams, enabling customers to optimize their own models.
Build robust evaluations for tracking modeling improvements.
Design, implement, test, and debug code across the research stack.

Requirements

Deep understanding of machine learning and machine learning applications.
Working knowledge of relevant models and methods for building evaluations to improve model capabilities.
Comfort diving into a large machine learning codebase to debug.
Ability to thrive in a dynamic and technically complex environment.

Benefits

Equity, performance-related bonuses for eligible employees, and competitive benefits.
Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
Pre-tax accounts for health, dependent care, and commuter expenses.
401(k) retirement plan with employer match.
Paid parental, medical, and caregiver leave.
Paid time off, company holidays, office closures, and paid sick or safe time.
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.