Research Engineer at Aisafety

Research Engineer

Aisafety · San Francisco, CA

Company

Aisafety

Location

San Francisco, CA

Type

Full Time

Job Description

Team: Research

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 Research Engineer here, you'll work at the intersection of cutting-edge ML research and reliable engineering. 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.

Key Responsibilities Include:

Own end-to-end research experiments.

Train and fine-tune large transformer models across domains.

Build and maintain 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 contributing to shared research direction and responding quickly in research cycles.

Support research infrastructure as needed, such as internal tooling, documentation, and reproducibility practices for the team.

You might be a good fit if you:

Masters in ML or related position and at least 2 years of research experience.

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 huggingface).

Have experience launching and training distributed ML jobs.

Communicate clearly and promptly with teammates.

Have co-authored an NLP or RL paper in a top conference.

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