AI Safety Research Careers

Technical AI safety research asks how AI systems fail, how risks can be measured and which interventions reduce those risks without hiding trade-offs. Current employer examples show several research directions. OpenAI’s Agent Safety role describes training, measurements and oversight work, including evaluations and system-level mitigations. Anthropic’s research and engineering listings cover areas such as alignment, interpretability, model evaluations and safeguards. These are role examples, not a permanent taxonomy or guarantee that every lab uses the same titles. This field is distinct from platform trust-and-safety operations, which may handle user reports, policy enforcement and abuse cases. It also narrows the broader AI-research career path: a safety researcher still needs sound experimental practice, but chooses questions about model behavior, misuse, robustness, oversight or control. A project should state its threat model, evaluation setup, failure criteria and limitations. Claims about safety should be tied to tested systems and conditions; passing one benchmark does not prove a system safe in general. Preparation can draw on machine learning, security, statistics, human-computer interaction or another relevant discipline. Roles may emphasize research papers, systems-building, evaluation design or empirical red-teaming. Build evidence through a replication, carefully documented evaluation, open-source tool, research contribution or relevant engineering project. Follow each employer’s current posting for degree and experience requirements. There is no one credential or course sequence that guarantees a safety-research job; demonstrate technical rigor and the ability to revise conclusions when evidence changes.