Curated roles
Browse roles focused on reducing risks from advanced AI systems, including alignment research, safety engineering, evaluations and related operations.
343 active roles found.
The Research Engineer role involves implementing and running safety evaluations on frontier AI models, focusing on loss-of-control and harmful manipulation risks.
Machine Learning Engineer building AI safety and security systems, including abuse detection, frontier model evaluations, and red teaming workflows.
The Research Scientist role involves leading research on risks in frontier AI models, designing evaluation methodologies, and authoring safety reports to advance AI safety.
The Research Engineer role involves implementing and running safety evaluations on frontier AI models, focusing on loss-of-control and harmful manipulation risks.
Research scientist role focused on empirical and conceptual research into AI well-being, moral status, and related safety/welfare questions.
Nonprofit role focused on building the technical AI safety talent pipeline through advising, courses, grants, and field strategy.
Strategic partnerships role for Scale's Red Team and Safety function, managing frontier-lab engagements focused on AI red teaming, adversarial evaluations, and safety testing of frontier models.
The role involves building systems for high-quality reinforcement learning data, focusing on AI safety research and ensuring the quality of training data.
The role involves analyzing user behavior data to provide insights on safety concerns and defining metrics to measure success in deploying safe AI systems.
This course focuses on AI security research, teaching participants about attacks on AI systems and defenses, including adversarial examples and model tampering.
The AI Red Teamer role involves evaluating the security and resilience of advanced AI systems through red team assessments, adversarial testing methodologies, and identifying vulnerabilities to ensure safe deployment.
Teacher role for an in-person bootcamp focused on technical AI safety instruction, facilitation, and curriculum development.
Technical AI safety role focused on full-stack alignment research, including compute-intensive engineering and conceptual work on pretraining and alignment interventions.
The role involves developing hardware architectures for verifying and securing advanced AI systems, collaborating with AI safety researchers, and translating AI security requirements into hardware implementations.
Program management role supporting an AI safety research extension program for fellows, with coordination, operations, workshops, and outcome tracking.
The role involves building safety systems and infrastructure for AI-driven media experiences, focusing on real-time content moderation, risk detection, and ensuring responsible deployment of generative media.
PhD studentship researching LLM safety via mechanistic interpretability, behavioral research, deceptive behavior detection, and inference-time monitoring to reduce model risk.
Principal full-stack engineer building an open source LLM research platform and evaluation tools for researchers to evaluate and understand model behavior.
The role involves designing and implementing safety alignment techniques for AI systems, creating datasets for safety alignment, and building infrastructure for safety evaluations.
The role involves leading the SPAR program, the largest AI safety research fellowship, focusing on strategic expansions, managing research managers, and cultivating relationships with AI safety researchers.