Senior Research Scientist

United States·San FranciscoFull-Timesenior

Data ScienceData ScientistResearch ScientistDataData & AI

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Overview

The Center for AI Safety (CAIS) is a leading research and advocacy organization focused on mitigating societal-scale risks from AI. We address AI’s toughest challenges through technical research, field-building initiatives, and policy engagement, along with our sister organization, Center for AI…

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As a Senior Research Scientist here, you will lead and execute high-impact research that advances the safety and reliability of frontier AI systems, taking ownership of ambitious open problems and seeing them through to publication. We expect senior scientists to set a high bar for research quality and push the team’s thinking forward. You’ll design and run experiments on large language models, build the tooling needed to train and evaluate models at scale, and turn results into publishable research. You’ll collaborate closely with CAIS researchers and external academic and commercial partners, using our compute cluster to run large-scale training and evaluation. The work spans areas like AI honesty, robustness, transparency, and trojan/backdoor behaviors, aimed at reducing real-world risks from advanced AI systems.

Pursue high-value research directions with limited guidance.

Own end-to-end research experiments with full accountability for quality and outcomes.

Train and fine-tune large transformer models across domains.

Build and maintain datasets and benchmarks.

Run distributed training and evaluation at scale.

Lead the writing and publication process on key papers, including driving submissions to top conferences.

Collaborate with researchers and external partners while contributing to shared research direction, responding quickly in research cycles, and elevating the quality of the team’s work.

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.