Why This Job is Featured on The SaaS Jobs
Work on synthetic reinforcement learning sits at an increasingly important intersection of SaaS and frontier-model deployment: improving how models are trained and evaluated upstream directly affects the reliability of AI capabilities that later show up in productized, API-driven services. The remit here spans synthetic data, simulators, and feedback loops—areas that shape not just model performance, but also how teams reason about measurement when real-world labels are limited or costly.
From a SaaS career standpoint, this role builds durable expertise in experimentation at scale: designing evaluations, interpreting learning dynamics, and translating research outcomes into training approaches that can be integrated into production pipelines. That combination—research rigor plus an orientation toward implementation—maps well to SaaS organizations that operationalize ML, where iteration cadence, reproducibility, and clear success metrics become as important as novel ideas.
This position is best suited to professionals who prefer open-ended problem spaces and are comfortable making progress amid evolving objectives and imperfect signals. It should appeal to researchers who want their work to influence deployed systems, and who enjoy close collaboration with engineers and other scientists to turn results into repeatable processes rather than one-off experiments.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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 or evolving
Are motivated by seeing research ideas influence real-world AI systems
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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