About the job
About Our Team
Join the Interpretability team at OpenAI, where we delve into the inner workings of deep learning models. Our mission is to leverage internal representations to gain insights into model behavior and to design models that offer clearer interpretations. We prioritize applying our findings to enhance the safety of advanced AI systems. Our collaborative and inquisitive work culture fosters innovation and exploration.
About the Position
OpenAI is on the lookout for a dedicated researcher with a passion for deep learning and a solid engineering background. In this role, you will develop and execute a research agenda focused on mechanistic interpretability, working closely with a team of driven individuals. Your contributions will be vital in ensuring that future AI models remain safe as their capabilities expand, significantly advancing our commitment to creating safe AGI.
Key Responsibilities:
Conduct and publish research on methods for interpreting the representations of deep networks.
Develop infrastructure to analyze model internals on a large scale.
Collaborate across various teams to undertake projects uniquely suited to OpenAI’s capabilities.
Direct research initiatives towards tangible usefulness and long-term scalability.
Ideal Candidate Profile:
Passionate about OpenAI’s mission to ensure that AGI benefits all of humanity, and aligned with OpenAI’s charter.
Enthusiastic about long-term AI safety and knowledgeable about the technical pathways to achieve safe AGI.
Experience in AI safety, mechanistic interpretability, or closely related fields.
Possess a Ph. D. or substantial research background in computer science, machine learning, or a related discipline.
Excited to engage with large-scale AI systems and utilize OpenAI’s exceptional resources in this domain.
Have 2+ years of experience in research engineering and proficiency in Python or similar programming languages.
Exhibit a deep curiosity and willingness to explore new ideas.
