Research Engineer Intern (Fall 2026)

Are a current student in machine learning or a related field. Exceptional candidates with a strong publication record may be considered regardless of degree level.

Have co-authored at least one paper published at a top ML conference venue (e.g., NeurIPS, ICML, ICLR, ACL, CVPR). Workshop papers are considered, though peer-reviewed conference publications are strongly preferred. Publications in journals such as IEEE or Springer Nature are typically given less weight.

Have a track record of empirical research in AI or ML, particularly in AI safety-relevant areas (e.g. adversarial robustness, calibration, benchmarking). We weight empirical research heavily; candidates with primarily theoretical backgrounds are generally not a strong fit.

Alternatively, have made meaningful research contributions at a leading AI lab.

Are able to read an ML paper, understand the key result, and understand how it fits into the broader literature.

Are comfortable setting up, launching, and debugging ML experiments.

Are familiar with relevant frameworks and libraries (e.g., PyTorch).

Communicate clearly and promptly with teammates.

Take ownership of your individual part in a project.