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
Machine Learning @ 6
Reinforcement Learning @ 6
- 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
The RL and Reasoning team drives the core reasoning paradigm and has created innovations such as o1 and o3. The team focuses on advancing reinforcement learning research, building next-generation generative models, and deploying them at scale.
As a Research Engineer/Research Scientist, you will advance the frontier of AI alignment and capabilities through cutting-edge reinforcement learning methods. Your work will contribute to training intelligent, aligned, and general-purpose agents, including the systems that power various models. The role requires a background in reinforcement learning research, the ability to iterate quickly, and strong coding proficiency.
Responsibilities
- Advance reinforcement learning methods for AI alignment and capabilities.
- Conduct research related to reinforcement learning and language models.
- Develop and improve systems for training intelligent, aligned, and general-purpose agents.
- Iterate quickly in a technically complex research environment.
- Dive into a large machine learning codebase to debug and improve it.
- Design principled approaches and controlled experiments that produce trustworthy conclusions.
Requirements
- Background in reinforcement learning research.
- Strong coding proficiency.
- Deep understanding of machine learning and machine learning applications.
- Interest in reinforcement learning and language model research.
- Ability to take initiative and ownership of ideas and drive them to completion.
- Ability to work effectively in a fast-paced, dynamic, and technically complex environment.
Work Arrangement
- Based in San Francisco, California.
- Hybrid work model with three days in the office per week.
Benefits
- Medical, dental, and vision insurance for employees and families, 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, 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.
- Equity, performance-related bonuses for eligible employees, and other taxable fringe benefits may be provided.
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