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# Researcher, Trustworthy AI Organization

- **Company:** OpenAI Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Files, Python (Programming Language), Software Safety, Scripting, Large Language Models
- **Published:** July 31, 2026
- **Apply:** https://www.careerbuilder.com/job-details/researcher-trustworthy-ai-san-francisco-ca--8d59eba6-4f1e-496c-a69b-f003b84858f0

## About the Role

* Are excited about OpenAI's mission of building safe, universally beneficial AGI and are aligned with OpenAI's charter
 * Demonstrate 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 datasets
 * Enjoy working on large-scale, difficult, and nebulous problems in a well-resourced environment
 * Exhibit proficiency in the field of AI safety, focusing on topics like RLHF, adversarial training, robustness, LLM evaluations
 * Have past experience in interdisciplinary research
 * Show enthusiasm for socio-technical topics, Affirmative Action, Artificial Intelligence (AI), Data Sets, Equal Employment Opportunity (EEO), Genetics, Large-Scale Systems, Python Programming/Scripting Language, Research Skills, Risk Modeling, Safety Systems, Safety/Work Safety, Scientific Research, Strategic Planning, Time Management

## Description

We 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 design
 * Build creative methods and run experiments that enable public input into model values
 * Increasing rigor of external assurances by turning external findings into robust evaluations
 * Facilitating and growing our ability to effectively de-risk flagship model deployments in a timely manner


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