Research Engineer Intern

Center for Ai Safety; San Francisco (CA); On-site; USD 9,700 - 19,000; Full time

Benefits offered by this job

Stipend

Job summary

The Center for AI Safety in San Francisco invites a Research Engineer Intern to work closely with our researchers on AI safety, ML alignment, and benchmarking. You will plan and run experiments, review code, and contribute to publications, using our compute cluster to scale experiments on large language models.

You will have a dedicated mentor and be treated as a colleague within a small team. Applicants must be enrolled in university for a full-time fall internship, with stipends ranging from

Qualifications

• Currently a student in machine learning or a related field.
• At least one paper published in a top ML conference (e.g., NeurIPS/ICML/ICLR/ACL/CVPR).
• Experience with empirical AI/ML research, especially safety-related areas.
• Able to read ML papers and relate results to literature.
• Comfortable setting up, launching, and debugging ML experiments.

Responsibilities

• Plan and run experiments with researchers on AI safety topics.
• Conduct code reviews and collaborate with a small team.
• Contribute to publications with outsized impact.
• Leverage internal compute cluster to scale experiments on LLMs.

Skills

Machine Learning

Empirical Research

PyTorch

Communication

Ownership

Education

Enrollment in university

Tools

PyTorch

Job description

Introduction The Center for AI Safety (CAIS) is a leading research and field-building organization on a mission to reduce societal-scale risks from AI. Alongside our sister organization, the CAIS Action Fund, we tackle the toughest AI issues with a mix of technical, societal and policy solutions.

As a research engineer intern here, you will work very closely with our researchers on projects in areas such as AI security, machine ethics, AI alignment, and benchmarking AI risks. We will assign you a dedicated mentor throughout your internship, but we will ultimately be treating you as a colleague. By this we mean, you will have the opportunity to debate for your own experiments or projects, and defend their impact. You will plan and run experiments, conduct code reviews, and work in a small team to create a publication with outsized impact. You will leverage our internal compute cluster to run experiments at scale on large language models.

This application is for the full-time fall internship position. Applicants must be enrolled in university to be considered. Applications are due by July 31, 2026.

You might be a good fit if you:

• 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.

$9,700 - $19,000 a year

This internship is unpaid; however, CAIS provides the above stipend to assist with academic pursuits and living expenses. The stipend is subject to tax.

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 [e-mail].

We value diversity and encourage individuals from all backgrounds to apply.