Requirements:

Bachelor's degree or equivalent practical experience.
2 years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
2 years of experience managing projects and defining project scope, goals, and deliverables.
2 years of experience utilizing Python and SQL to solve engineering problems.
Experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

Nice to haves:

Experience with scripting language, machine learning tools, techniques and systems (including prompt engineering and fine-tuning, Large Language Model (LLMs)).
Knowledge of current abuse techniques and practices with the ability to extract, manipulate, and apply machine learning techniques to high volumes of critical, product-related data.
Ability to work with a variety of engineering stakeholders to gather requirements, explain models, and iterate to make improvements.
Excellent written and verbal communication and ability to describe technical implementations or analyses to a non-technical audience in an effective manner.
Excellent problem-solving and critical thinking skills with attention-to-detail in an ever-changing environment.

Develop scalable safety solutions for AI products across Google by leveraging advanced machine learning and AI techniques.
Apply statistical and data science methods to thoroughly examine Google's protection measures, uncover potential shortcomings, and develop actionable insights for continuous security enhancement.
Compute statistics (e.g., precision, recall) about the performance of technical models (ML systems, classifiers, Large Language Model (LLMs)) and recognize nuanced, easily missed problems in the performance of these systems.
Lead multiple interdependent projects of moderate to high-complexity and scope, work cross-functionally to ensure co-ordination and alignment on objectives and key results.