Senior Research Engineer

Key Responsibilities Include:

Own end-to-end research experiments, from premise to publication.
Train and fine-tune large transformer models across domains.
Own the design and maintenance of datasets and benchmarks.
Run distributed training and evaluation at scale.
Write and ship research, collaborating with co-authors, and supporting submissions of papers to top conferences.
Collaborate with researchers and external partners while helping drive shared research direction and responding quickly in research cycles.
Take ownership of research infrastructure as needed, such as internal tooling, documentation, and reproducibility practices for the team.

You might be a good fit if you:

• Are a current PhD student or researcher 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.