Engineering Analyst, Egregious Harms

About the job

Trust & Safety team members are tasked with identifying and taking on the biggest problems that challenge the safety and integrity of our products. They use technical know-how, excellent problem-solving skills, user insights, and proactive communication to protect users and our partners from abuse across Google products like Search, Maps, Gmail, and Google Ads. On this team, you're a big-picture thinker and strategic team-player with a passion for doing what’s right. You work globally and cross-functionally with Google engineers and product managers to identify and fight abuse and fraud cases at Google speed - with urgency. And you take pride in knowing that every day you are working hard to promote trust in Google and ensuring the highest levels of user safety.

As an Engineering Analyst within the Trust and Safety team, you will be part of a global team that ensures the seamless detection and enforcement of some of the most egregious online harms (e.g., child sexual abuse materials and non-consensual intimate imagery) through close partnerships with Product, Engineering, Policy, and Legal, you will leverage your technical acumen to design and develop novel solutions to further Google’s ability to fight this abuse worldwide.

At Google we work hard to earn our users’ trust every day. Trust & Safety is Google’s team of abuse fighting and user trust experts working daily to make the internet a safer place. We partner with teams across Google to deliver bold solutions in abuse areas such as malware, spam and account hijacking. A team of Analysts, Policy Specialists, Engineers, and Program Managers, we work to reduce risk and fight abuse across all of Google’s products, protecting our users, advertisers, and publishers across the globe in over 40 languages.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Ireland: €90000 - €92000 (EUR) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• 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.

Minimum qualifications:

• 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).

Preferred qualifications:

• 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.

• 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).