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About the Role

As a Research Engineer (RE) or Research Scientist (RS) at CAIS, you will lead and execute high-impact research to advance the safety and reliability of frontier AI systems. This role involves designing and running experiments on large language models, building tooling for model training and evaluation at scale, and producing publishable research. You will collaborate with internal researchers and external partners, utilizing our compute cluster for large-scale training and evaluation. The work centers on empirical deep learning research with large language models and/or multimodal models.

Responsibilities

Own research experiments end-to-end.
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.
Mentor, guide, and support others on the team.

Requirements

Have co-authored multiple papers published at a top ML conference venues (e.g., NeurIPS, ICML, ICLR, ACL, CVPR).
Have made meaningful research contributions at a leading AI lab.
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 a track record of empirical research in AI or ML, particularly in AI safety-relevant areas (e.g. adversarial robustness, calibration, benchmarking).
Candidates with primarily theoretical backgrounds are generally not a strong fit.
Able to read an ML paper, understand the key result, and understand how it fits into the broader literature.
Comfortable setting up, launching, and debugging ML experiments.
Familiar with relevant frameworks and libraries (e.g., PyTorch).
Communicate clearly and promptly, and take ownership of your part of a project.

Skills

Empirical deep learning research
Large language models
Multimodal models
PyTorch

Location

Remote

Work Type

Full-time

Experience Level

PhD student or researcher in machine learning or a related field
Exceptional candidates with a strong publication record

Education Level

Salary/Compensations

$140,000 - $200,000 a year

Benefits

Health insurance for you and your dependents
401K plan + 4% matching
Unlimited PTO
Lunch and dinner at the office
Annual Professional Development Stipend

About the Company

The Center for AI Safety (CAIS) is a leading research and advocacy organization focused on mitigating societal-scale risks from AI.
We address the toughest challenges in AI safety through technical research, field-building initiatives, and policy engagement, along with our sister organization, Center for AI Safety Action Fund.
Our work is aimed at reducing real-world risks from advanced AI systems.
We deliberately pursue research directions that the field is not yet paying attention to, and we move on once the rest of the field catches up.
Our focus is on problems that are both highly important and highly neglected—and our track record is built on getting to them first.
Our work tends to be timely and to open up territory rather than crowd into it.
Our papers have repeatedly gone on to become widely cited and to set the standard for underexplored research areas.
Our work is regularly used by AI safety institutes and frontier AI labs, and they have shaped real policy outcomes, including being presented directly to senators and policymakers.

Equal Opportunity

The Center for AI Safety is an Equal Opportunity Employer.
We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, ancestry, age, disability, medical condition, marital status, military or veteran status, or any other protected status in accordance with applicable federal, state, and local laws.
In alignment with the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.
If you require a reasonable accommodation during the application or interview process, please contact contact@safe.ai.
We value diversity and encourage individuals from all backgrounds to apply.