Research Engineer Intern

Summary

The Center for AI Safety (CAIS) is a leading research organization focused on reducing societal-scale risks from AI. As a research engineer intern, you will collaborate with researchers on various AI-related projects and gain hands-on experience in planning experiments and contributing to impactful publications.

Responsibilities

• Work closely with researchers on projects in areas such as AI security, machine ethics, AI alignment, and benchmarking AI risks
• Plan and run experiments, conduct code reviews, and work in a small team to create a publication with outsized impact
• Leverage internal compute cluster to run experiments at scale on large language models

Skills

• Are a current student 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

Qualifications

Must Haves

• Are a current student 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

Benefits

• CAIS provides the above stipend to assist with academic pursuits and living expenses.
• The stipend is subject to tax.
• If we end up hiring your referral, you’ll receive a $1,500 bonus once they’ve been with CAIS for 90 days.