Researcher, Synthetic RL
OpenAI - San Francisco, California, United States
Posted Jan 9, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
- Not verified checked Jun 7, 2026
- Salary
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- 401(k) match
- Reported from DOL Form 5500 industry filing (not employer-specific)
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Role
- Seniority
- Mid From the posting source checked Jun 20, 2026
Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Researcher, Synthetic RL San Francisco, California, United States About the Team The Synthetic RL team develops reinforcement learning methods that leverage synthetic data, environments, and feedback to train and evaluate frontier AI models. The team explores approaches such as self-play, simulators, and other synthetic evaluations to push model capability, generalization, and alignment beyond what is possible with the current prevailing methodology. About the Role As a Research Scientist on the Synthetic RL team, you will develop novel reinforcement learning techniques that use synthetic environments and feedback to improve large-scale models. You'll work closely with other researchers to design experiments, analyze learning dynamics, and translate research insights into training approaches used in production systems. We're looking for researchers who enjoy working on open-ended problems, value fast iteration, and want their work to directly shape how frontier models are trained. 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: - Research and develop reinforcement learning algorithms - Design and run experiments to study training dynamics and model behavior at scale - Collaborate with engineers and researchers to integrate successful approaches into model training pipelines You might thrive in this role if you: - Have a strong background in reinforcement learning, machine learning research, or related fields - Have strong engineering and statistical analysis skills - Enjoy exploring new problem spaces where data, objectives, and evaluation are imperfect
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