About the job
About Our Team
The Alignment Training team at OpenAI focuses on understanding how advanced models develop lasting behavioral patterns throughout the training process. We investigate which behaviors can be influenced during the pre-training, mid-training, and post-training phases; create the necessary data, objectives, and evaluations to guide these behaviors; and assess whether the resulting actions represent a general capability or a byproduct of the training environment.
Our research encompasses synthetic data development, various training stages, model behavior analysis, and performance evaluation. We explore how models grasp user intentions, adhere to instructions, reason effectively, demonstrate honesty, and maintain reliability in novel situations. Our ultimate aim is to foster desirable behaviors early in training, reinforce them throughout, and ensure their consistency in real-world applications.
About This Position
We are seeking a seasoned researcher with profound expertise in large-scale model training, synthetic data creation, or evaluation processes, who is passionate about exploring how training decisions influence aligned behaviors in state-of-the-art models.
In this role, you will define the research agenda for alignment training: outlining the behaviors we aspire for models to acquire, designing data and training strategies to cultivate them, and developing evaluation mechanisms to verify the breadth, strength, and durability of those behaviors. The ideal candidate will excel at translating vague behavioral inquiries into structured experimental plans: devising hypotheses, creating interventions, establishing pipelines, conducting experiments, and analyzing results for authenticity.
This position is particularly suited for individuals eager to engage closely with the core model training framework, where decisions regarding data, objectives, and evaluations critically influence the alignment of deployed systems.
Key Responsibilities:
Innovate synthetic data methods that instill higher-level behavioral tendencies in models, such as comprehending user intent, consistently following instructions, clear reasoning, honesty, and alignment with defined goals and constraints.
Analyze the impact of pre-training, mid-training, and post-training on subsequent model behavior, identifying the most effective interventions for each phase.
Develop evaluation loops that link model behavior back to training data and objectives, enabling quicker iterations and clearer feedback.
