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Research Scientist
Center for AI Safety
Research Scientist
This job is still taking applications, but it's been up a while.
About the job
CAIS is a leading organization focused on mitigating societal risks from AI through research, policy, and initiatives. The role involves advancing AI safety, robustness, transparency, and reducing real-world AI risks, collaborating with external partners and utilizing large-scale compute resources.
Requirements
- Empirical research in AI or ML
- Experience with large language models
- Familiar with PyTorch or similar
- Strong publication record in top ML conferences
- Ability to design and run experiments
Qualifications
- Current PhD student or researcher
- Published in top ML venues
- Track record in AI safety-related research
- Strong understanding of ML literature
- Experimental setup and debugging skills
Full job description
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.
Mentor and guide others on 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.
Know someone who could be a great fit for this role? Submit their details through our Referral Form. If we end up hiring your referral, you’ll receive a $1,500 bonus once they’ve been with CAIS for 90 days.
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.
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