OpenAI - Protection Scientist Engineer, Integrity

• Define and drive protection strategy for a harm area or launch. Translate ambiguous risks into goals, priorities and roadmaps, and make trade-offs based on evidence, risk and available capacity. • Directly build and maintain harm detection. Develop production signals, classifiers and pipelines using SQL, Python, machine learning and language models, integrating with shared engineering platforms. • Lead cross-functional execution. Translate between specialist roles, establish shared understanding and ownership, resolve technical and organisational dependencies, and communicate recommendations and specific asks clearly. • Investigate emerging harms and abuse patterns. Work with world class domain experts to distinguish harmful activity from legitimate use, develop detection criteria, and inform policies and review protocols with Policy and Operations partners. • Evaluate and improve the complete system of defence. Measure remaining harm, detection quality and intervention effectiveness and collaborate with Data Science on more sophisticated metrics. Use expert feedback and operational outcomes to guide improvements. • Protect product and model launches. Define data, detection and review requirements; establish monitoring; investigate gaps and critical escalations; and drive improvements informed by post-launch findings. • Scale effective approaches with language models, agents and reusable tools. Choose where automation and human judgement are needed, and build documentation and processes that improve reliability and help others extend the work. • Think in systems. You have enough breadth across technical, analytical, investigative and operational disciplines to understand how they fit together, recognise gaps and translate between specialist perspectives. • Bring deep expertise in at least one relevant harm domain, developed through integrity, trust and safety, security, investigations or related work. You approach unfamiliar problems with curiosity and respect for expert knowledge and you are driven by reducing harm. • Are proficient in SQL and Python and have experience writing reliable and efficient production code for detection and data processing. Additional ML or SWE experience is a plus. • Can apply quantitative analysis and machine-learning methods to measure risks and evaluate system effectiveness. You understand the limitations of your data and can reason about missed harms, false positives and appropriate interventions. Additional DS/measurement background is a plus. • Have experience setting strategy and delivering complex, cross-functional technical projects. You can turn an ambiguous problem into a plan, align people with different priorities and drive work through to completion. • Communicate clearly and concisely in writing and conversation. You can explain technical findings, make actionable recommendations and surface uncertainty and trade-offs to different audiences. • Can use language models and other automation to scale analytical, investigative or operational work, with evaluation and human oversight appropriate to the problem.