Research Engineer / Scientist, Safety Reasoning

As a Research Engineer / Scientist at OpenAI, you will play a crucial role in improving the safety capabilities of foundational models. Your responsibilities will include conducting applied research focused on reasoning about human values and cultural norms, developing moderation models to address AI misuse, and collaborating with policy researchers to iterate on content policies. Additionally, you will work on multimodal content analysis and enhance pipelines for data labeling and model training, ensuring our safety systems are robust and effective.

To be considered for the Research Engineer / Scientist role at OpenAI, candidates should ideally possess over 5 years of research engineering experience. Proficiency in Python or similar programming languages is essential, along with a deep understanding of AI safety topics such as robustness, fairness, and biases. Familiarity with large-scale AI systems and multimodal datasets is highly advantageous, as is a passion for enhancing the safety of AI models for their real-world applications.

AI safety is a core component of the Research Engineer / Scientist role at OpenAI. The successful candidate will engage in developing and applying machine learning techniques that prioritize safe deployments of AI. You will be tasked with improving foundational models to accurately understand safety-related queries and issues, which in turn informs effective moderation and policy frameworks aimed at mitigating AI misuse. This collaborative effort directly supports OpenAI's mission to foster safety and trust in AI technologies.

In this exciting role at OpenAI, you will engage in various projects focused on enhancing AI's understanding of safety, moderation policies, and cultural norms. Specific projects may include developing AI moderation frameworks, executing applied research to improve reasoning capabilities, and designing robust testing avenues for our AI systems. You'll also contribute to active learning processes and the refinement of content analysis, all of which support the overarching mission of creating a safe AI landscape.

OpenAI is committed to fostering a supportive environment for research and professional development. As a Research Engineer / Scientist, you will have opportunities for continued learning through collaborations on innovative projects, access to resources, and the ability to engage with leading experts in the field. OpenAI encourages a culture of discovery, allowing you to explore new research techniques and methodologies that contribute to your personal and professional advancement within the AI safety domain.

When discussing your experience, focus on specific machine learning techniques you've used, such as reinforcement learning, adversarial training, or failure mode analysis. Highlight any projects where you've applied these techniques to promote safety outcomes, mentioning challenges faced and how you overcame them. Be prepared to share examples and quantify your impacts.

Share your passion for ensuring AI benefits humanity and discuss any specific experiences that ignited your interest in AI safety. Explain how these motivations align with OpenAI’s mission to build safe AGI and address societal challenges like misinformation and misuse.

Discuss a particularly challenging project you've worked on, including the context, objectives, and specific challenges encountered. Articulate your problem-solving approach and the strategies employed to align AI behavior with safety, and what the outcomes were, emphasizing lessons learned.

Indicate that you actively engage with academic journals, attend conferences, participate in relevant forums, and connect with colleagues in the field. Mention any specific publications or topics you follow and how you incorporate new findings into your work.

You should explain a structured approach to evaluating AI models, such as using metrics that measure robustness, fairness, and error rates. Discuss specific techniques like simulation tests, red-teaming exercises, or user feedback mechanisms and how these contribute to model safety.

Highlight any specific projects that involve multimodal datasets, explaining the types of data used and the methodologies applied. Discuss the challenges inherent in analyzing diverse data forms and how your insights contributed to safety enhancements.

Discuss the importance of incorporating safety checks throughout the innovation process. Explain how you approach innovation with a mindset prioritizing ethical considerations and the responsible development of AI, ensuring that any advancements align with safety standards and societal values.

Discuss how you establish open lines of communication, engage in joint project activities, and utilize collaborative tools to align engineering efforts with policy objectives. Highlight your flexibility and responsiveness to iterative feedback in the policy development process.

Explain the tools and techniques you've used for automated data labeling and how they've enhanced efficiency in your projects. Discuss the importance of accurate labeling in safety contexts and any challenges you faced in implementing augmentation processes.

Provide an informed perspective on current challenges, such as tackling biases in AI, misinformation, or the misuse of technology. Articulate your thoughts on how OpenAI can address these challenges through research, collaboration, and innovative solutions.