As a Staff Engineering Analyst, you will serve as an organizational technical leader driving the multi-quarter roadmap for scaled GenAI safety evaluation across Google. Working with minimal direction, you will bridge GDM, Product Area engineering, and Trust and Safety—architecting end-to-end evaluation infrastructure, leading quantitative gap analyses, and mentoring Engineering Analysts across the team.
At Google we work hard to earn our users’ trust every day. Trust and 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.US: $188000 - $274000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Define and advocate the Trust and Safety (T&S) RAI SCALE technical roadmap, establishing North Star metrics and automated governance to accelerate Google-wide launch velocity.
- Direct the research-to-operations architecture translating high-risk 0-to-1 evaluations (multi-turn, agentic, frontier safety) into production-ready pipelines.
- Lead cross-organizational gap analyses with GDM/PAs, defining statistical standards and scaling high-precision LLM-as-a-judge autoraters.
- Architect unified data pipelines and telemetry unifying pre-launch testing and post-launch monitoring to automate executive risk attestations.
- Serve as the final decision-maker on critical GenAI safety escalations, align executive leadership, and mentor executive Engineering Analysts. Work with sensitive content or situations and may be exposed to graphic, controversial, or upsetting topics or content.
Minimum qualifications:
- 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.
Preferred qualifications:
- Master's degree in a quantitative discipline.
- 7 years of experience or familiarity with machine learning systems.
- 7 years of experience or familiarity with one or more of the following languages: SQL, R, Python, or C++.
- Excellent problem-solving and critical thinking skills with attention to detail in an ever-changing environment.
- Excellent written and verbal communication skills.