Requirements:

Bachelor's degree or equivalent practical experience.
7 years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
7 years of experience managing projects and defining project scope, goals, and deliverables.
7 years of experience with one or more of the following languages: SQL, R, Python, or C++.
5 years of experience working in a Trust and Safety Operations, data analytics, cybersecurity, or other related environment.

Preferred qualifications:

Master's degree or PhD in a quantitative or engineering field.
Experience in designing and conducting experiments or quantitative research, in a technology or AI context.
Experience working with Large Language Models, LLM Operations, prompt engineering, pre-training, and fine-tuning.
Understanding of AI systems, machine learning, and their potential risks.
Excellent problem-solving and critical thinking skills with attention to detail and the ability to think strategically and identify emerging threats and vulnerabilities.
Excellent communication and presentation skills and the ability to influence cross-functionally at various levels.

Drive structured and unstructured testing of novel model modalities and capabilities, collaborating with Google DeepMind.
Lead platform and tooling development, designing engineering solutions and prompt generation strategies, leveraging Large Language Models (LLMs) to improve adversarial testing and bridge technical constraints.
Define and ensure adherence to testing and safety standards in collaboration with cross-functional teams, including policy and engineering.
Perform analyses, develop insights for model and product-level safety mitigations, and influence safety initiatives across Product, Engineering, Research, and Policy. Act as a key advisor to leadership on complex safety issues and represent Google's AI safety efforts externally.
Be exposed to graphic, controversial, or upsetting content.