Researcher, Trustworthy AI

OpenAI
Safety SystemsPosted 23 February 2026

Job Description

About the teamThe Safety Systems team is responsible for various safety work to ensure our best models can be safely deployed to the real world to benefit the society and is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency.The Trustworthy AI team works on action relevant or decision relevant research to ensure we shape A(G)I keeping societal impacts in mind. This includes work on full stack policy problems such as building methods for public inputs into model values and understanding impacts of anthropomorphism of AI. We aim to translate nebulous policy problems to be technically tractable and measurable. We then use this work to inform and build interventions that increase societal readiness for increasingly intelligent systems. Our team also works on external assurances for AI with an aim for increasing independent checks and forming additional layers of validation.About the roleWe are looking to hire exceptional research scientists/engineers that can push the rigor of work needed to increase societal readiness for AGI. Specifically, we are looking for those that will enable us to translate nebulous policy problems to be technically tractable and measurable.This role is based in our San Francisco HQ. We offer relocation assistance to new employees.In this role, you will enable: Set research and strategies to study societal impacts of our models in an action-relevant manner and figure out how to tie this back into model designBuild creative methods and run experiments that enable public input into model valuesIncreasing rigor of external assurances by turning external findings into robust evaluationsFacilitating and growing our ability to effectively de-risk flagship model deployments in a timely mannerYou might thrive in this role if you: Are excited about OpenAI’s mission of building safe, universally beneficial AGI and are aligned with OpenAI’s charterDemonstrate a passion for AI safety and making cutting-edge AI models safer for real-world use.Possess 3+ years of research experience (industry or similar academic experience) and proficiency in Python or similar languages Thrive in environments involving large-scale AI systems and multimodal datasetsEnjoy working on large-scale, difficult, and nebulous problems in a well-resourced environmentExhibit proficiency in the field of AI safety, focusing on topics like RLHF, adversarial training, robustness, LLM evaluationsHave past experience in interdisciplinary researchShow enthusiasm for socio-technical topicsAbout OpenAIOpenAI 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, adver ... (truncated, view full listing at source)
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