Researcher, Pretraining Safety

About the Team

The Safety Systems team is responsible for various safety work to ensure our best models can be safely deployed to the real world to benefit 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 Pretraining Safety team’s goal is to build safer, more capable base models and enable earlier, more reliable safety evaluation during training. We aim to:

• Develop upstream safety evaluations that monitor how and when unsafe behaviors and goals emerge;
• Create safer priors through targeted pretraining and mid-training interventions that make downstream alignment more effective and efficient
• Design safe-by-design architectures that allow for more controllability of model capabilities

In addition, we will conduct the foundational research necessary for understanding how behaviors emerge, generalize, and can be reliably measured throughout training.

About the Role

The Pretraining Safety team is pioneering how safety is built into models before they reach post-training and deployment. In this role, you will work throughout the full stack of model development with a focus on pre-training:

• Identify safety-relevant behaviors as they first emerge in base models
• Evaluate and reduce risk without waiting for full-scale training runs
• Design architectures and training setups that make safer behavior the default
• Strengthen models by incorporating richer, earlier safety signals

We collaborate across OpenAI’s safety ecosystem—from Safety Systems to Training—to ensure that safety foundations are robust, scalable, and grounded in real-world risks.

In this role, you will:

• Develop new techniques to predict, measure, and evaluate unsafe behavior in early-stage models
• Design data curation strategies that improve pretraining priors and reduce downstream risk
• Explore safe-by-design architectures and training configurations that improve controllability
• Introduce novel safety-oriented loss functions, metrics, and evals into the pretraining stack
• Work closely with cross-functional safety teams to unify pre- and post-training risk reduction

You might thrive in this role if you:

• Have experience developing or scaling pretraining architectures (LLMs, diffusion models, multimodal models, etc.)
• Are comfortable working with training infrastructure, data pipelines, and evaluation frameworks (e.g., Python, PyTorch/JAX, Apache Beam)
• Enjoy hands-on research — designing, implementing, and iterating on experiments
• Enjoy collaborating with diverse technical and cross-functional partners (e.g., policy, legal, training)
• Are data-driven with strong statistical reasoning and rigor in experimental design
• Value building clean, scalable research workflows and streamlining processes for yourself and others