Senior Research Engineer

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 Safety Action Fund.

 

As a Senior Research Engineer here, you’ll work at the intersection of cutting-edge ML research and reliable engineering. You’ll take full ownership of research projects, from premise to publication, and be expected to drive them relatively autonomously with input from an advisor. 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.

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.

Have a Master’s or Ph.D. in ML or a related field and at least 4 years of research experience.

Have published multiple ML papers at top conferences.

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

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

Have experience launching and training distributed ML jobs.

Communicate clearly and promptly with teammates.

Have demonstrated strong research ability, and are comfortable managing one or more projects relatively autonomously while incorporating feedback from collaborators or advisors.