Ai Safety Residency - Slprep

About the ProgramThe
Human Frontier Collective (HFC)
brings researchers and scientists together to tackle consequential problems in AI. In partnership with
Scale Labs Preparedness and Policy (SLPreP), we are building a small cohort of researchers to work on the empirical foundations of AI safety.
The work includes thinking about various failure modes, evaluating whether safety controls hold up under pressure in realistic settings, and studying how AI agents (mis-)behave over long-horizon tasks. Residents take open research questions like these and turn them into work that improves how frontier AI systems are evaluated and safeguarded.
Research AreasMentors will guide AI safety residents on projects in AI safety research areas, such as:
AI control and chain-of-thought monitoring:
Developing techniques to oversee and constrain AI systems, including monitoring their reasoning to detect harmful intent or deception.
Alignment red-teaming:
Developing science around testing whether models have hidden goals, misaligned behaviors or unsafe tendencies.
Multi-agent safety:
Studying the risks that emerge when multiple agents interact, such as collusion, coordinated failures, or unexpected collective behavior.
Recursive Self-Improvement (RSI) safety:
Investigating how to keep AI systems safe and aligned as AIs increasingly contribute to their own development.
Science of Evaluations:
Examining when and how models recognize they're being tested, and how situational awareness affects the reliability/validity of evaluations.
Misuse risks:
Developing methods to assess how AI systems could be exploited by malicious actors, including evaluating their potential to enable harmful activities.
Projects are matched to demonstrated expertise;
we don’t expect any candidate to cover every area.
Representative Research
ASPI : A benchmark with task–attack scenarios testing how clarification‑seeking changes prompt‑inject” vulnerability in LLM agents.
PropensityBench : evaluating whether language models choose harmful actions when given simulated dangerous tools under various operational pressures
BioRiskEval : evaluating whether safeguards for open‑weight bio‑foundation models remain robust under fine‑tuning and probing.
RSI Control : evaluating whether frontier models can covertly sabotage R&D efforts in the RSI loop.
See more work from the Human Frontier Collective.
What You'll DoProjects are mentor‑guided. Each resident will be given a list of research proposals and match you with one based on your research interests and background. You’ll meet weekly with a researcher from SLPreP who sets the direction and gives feedback, and you will drive the day‑to‑day work. By default, the project is expected to result in a conference paper.
Own research end to end:
Turn a high‑level safety question into hypotheses, baselines, and controlled experiments, and produce work that others can inspect and build on.
Build the measurement:
Implement and debug evaluation harnesses, agent environments, datasets, monitoring methods, or post‑training experiments.
Exercise research judgment:
Identify confounds, separate what a model can do from what it tends to do, make sense of negative results, and decide what to try next without step‑by‑step direction.
Make the work legible:
Produce reproducible code, experiment logs, analysis, and precise technical writing. External outputs, where appropriate, remain subject to research, security, and communications review. Outputs also include research papers, which will be submitted to top ML conferences (NeurIPS, ICML, ICLR).
Who We're Looking ForYou may be a good fit if you:
are motivated by ensuring the safe and responsible development of advanced AI systems;
are interestedin technical safety research, such as alignment evaluations, AI control, monitoring, multi‑agent safety, or open‑weight model safety;
are pursuing or have completed a PhD in CS, ML, statistics, mathematics, or a related field (we also encourage researchers with an equivalent track record to apply!);
can make progress with high‑level guidance, including implementing, debugging, getting unstuck, interpreting results, and proposing next experiments;
communicate research clearly and handle confidential information and internal resources responsibly.
About the Role
Location:
Remote within the United States, including California and New York
Hours:
20 hours per week
Term:
3 months, with the option to extend to 6–12 months for exceptional candidates (part‑time Internship structure)
Compensation:
$100/hour
PLEASE NOTE:
Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high‑quality data and full‑stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at View email address on ca.hrm.tc. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.
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