Postdoctoral Research Fellow, Data and Intelligent Systems (DaIS)

Ideal candidates will have a strong interest and demonstrated experience in AI alignment, including areas such as robustness, interpretability, safety, evaluation, and the alignment of advanced AI systems with human values. Research may span theoretical foundations, empirical methods, and system-level approaches to ensuring reliable and trustworthy AI. Ongoing work includes alignment challenges in language models, multimodal systems, code generation, and scientific AI, with an emphasis on safety, fairness, and responsible deployment.

Postdoctoral Research Fellows will be appointed through Data and Intelligent Systems (DaIS) and will receive a competitive salary, benefits, office space and equipment, dedicated compute resources, and annual research support. Appointments will be made a the Postdoctoral Research Associate rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding.

Postdoctoral requirements: Princeton University places high value on in-person collaborations and interactions, and we expect selected candidates to participate in-person. The work location for this position is in-person on campus at Princeton University.

This position is subject to the University’s background check policy.

These positions are not eligible for sponsorship of an H-1B visa requiring consular processing; other visa sponsorships (including H-1B visas not requiring consular processing) may be available, as appropriate., $100.00

Join Princeton AI Alignment and Safety (AIAS) to advance the frontier of AI Alignment as DaIS Postdoctoral Research Fellows at Princeton University.

DaIS Postdoctoral Research Fellows will be part of a dedicated research community that includes leading faculty, research fellows, scientists, software engineers, postdocs, and graduate students working at the forefront of AI alignment. Fellows will have access to a state-of-the-art Princeton AI GPU cluster to support large-scale experimentation and evaluation.