Ai Safety Research Resident

Research AI safety remotely from the United States with HFC and SLPreP, focusing on evaluations, monitoring, and agent behavior.This part-time residency supports researchers pursuing or holding a PhD in computer science, machine learning, statistics, mathematics, or related fields. Residents investigate empirical foundations of AI safety, guided weekly by an SLPreP researcher while driving daily work. Projects may address AI control, alignment red-teaming, multi-agent safety, recursive self-improvement, evaluations, or misuse risks.Key Responsibilities:
Turn safety questions into hypotheses, baselines, and controlled experimentsImplement and debug evaluation harnesses, environments, datasets, and monitoring methodsIdentify confounds and interpret negative research resultsPropose next experiments with high-level guidanceProduce reproducible code, logs, analysis, and technical writingRequirements:
Pursuing or completed PhD, or equivalent research track recordInterest in technical AI safety researchAbility to progress independently with high-level guidanceClear research communication skillsResponsible handling of confidential information and internal resourcesBuild empirical AI safety research through evaluations, monitoring, controlled experiments, and precise technical communication.
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