Research Engineer/Scientist

About the organization

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, alongside our sister organization, Center for AI Safety Action Fund.

Our work distinguishes itself by targeting real-world risks from advanced AI systems, prioritizing problems that are both highly important and highly neglected. We focus on research directions that the field is not yet addressing, moving on once others catch up. Our track record includes pioneering work in AI honesty, robustness, transparency, trojan/backdoor behaviors, malicious use, weaponization capabilities, and AI value systems. Our research has set benchmarks, shaped policy outcomes, and been widely cited by AI safety institutes and frontier AI labs.

About the role

As a Research Engineer (RE) or Research Scientist (RS) at CAIS, you'll lead and execute high-impact research that advances the safety and reliability of frontier AI systems. This posting covers both roles; we will determine the best fit during the process.

You will design and run experiments on large language models, build tooling to train and evaluate models at scale, and publish your results. You’ll collaborate with CAIS researchers and external partners, using our compute cluster for large-scale training and evaluation. Our work centers on empirical deep learning research with large language models and/or multimodal models. Research directions are set by our Research Director based on importance, neglectedness, and timeliness, ensuring you work on high-impact, underexplored problems with substantial freedom in approach.

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 submitting papers to top conferences.
• Collaborate with researchers and external partners to shape research direction and respond quickly in research cycles.
• Support research infrastructure, including internal tooling, documentation, and reproducibility practices.
• Mentor, guide, and support other team members.

Qualifications

• Have co-authored multiple papers published at top ML conference venues (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. Alternatively, 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). Empirical research is heavily weighted; candidates with primarily theoretical backgrounds are generally not a strong fit.
• Can read an ML paper, understand the key result, and contextualize it within 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, and take ownership of your part of a project.

Benefits

• Health insurance for you and your dependents.
• 401K plan + 4% matching.
• Unlimited paid time off (PTO).
• Lunch and dinner provided at the office.
• Annual Professional Development Stipend.
• Access to top talent in technical and conceptual AI safety research.
• Referral bonus: $1,500 if your referral is hired and stays for 90 days.

Pay

$140,000 - $200,000 a year.