Senior Engineering Analyst, Egregious Harms

Google is looking for a Senior Engineering Analyst to join its Trust & Safety team on site in Zürich, ZH. This is a full-time role based in person, working with engineers, product managers, policy experts, and legal colleagues to tackle some of the internet’s most challenging safety issues. The team operates globally to protect users and partners across Google products like Search, Maps, Gmail, and Google Ads, and they value clear thinking, practical problem-solving, and a bias for getting things done.

The ideal candidate is drawn to high-stakes safety problems, comfortable with data-driven decision making, and eager to collaborate with a wide range of technical and non-technical teammates. You’ll be part of a globally distributed group that designs and implements solutions to fight abuse at Google speed, with a strong emphasis on integrity and user trust. You should be ready to engage with sensitive material as part of this critical mission.

As part of the Trust & Safety group, the role involves working with a global network of professionals to detect and enforce against some of the most egregious online harms, including material related to child exploitation and non-consensual imagery. The position includes exposure to graphic, controversial, or upsetting content, and you’ll contribute to protecting users, advertisers, and publishers across Google’s broad ecosystem.

What you’ll do day to day

You’ll build scalable safety solutions for Google’s AI products by applying advanced machine learning techniques and robust engineering practices. The work blends data science with hands-on ML design to improve protection measures, uncover weaknesses, and translate findings into practical enhancements across products. You’ll also examine model performance using statistical methods, identify nuanced issues in ML systems, and report insights that help drive security improvements across the platform. Across multiple projects, you’ll coordinate with product, engineering, policy, and legal teams to keep objectives aligned and ensure progress stays on track.

Must-have qualifications

• A bachelor’s degree or practical experience that demonstrates equivalent knowledge.
• Five years of data analysis experience, with a track record of identifying trends, generating summary statistics, and drawing actionable insights from both quantitative data and qualitative inputs.
• Five years of leading projects, defining scope, setting goals, and specifying deliverables.
• Hands-on work with machine learning design and the infrastructure needed to deploy, evaluate, process data, debug, and fine-tune models.
• Five years of using Python and SQL to solve engineering problems.

Nice-to-have capabilities

• Experience with scripting for ML workflows and tools, including prompt engineering and fine-tuning large language models.
• Knowledge of current abuse techniques and the ability to apply ML approaches to large, mission-critical product data.
• Comfort working with a range of engineering stakeholders to gather requirements, explain models, and iterate to improve outcomes.
• Excellent written and verbal communication skills, with the ability to present technical concepts to non-technical audiences effectively.
• Strong problem-solving and critical-thinking abilities, with careful attention to detail in a changing environment.

Practical details you should know

The role is full-time and onsite in Zürich, and it sits within Google’s Trust & Safety team, which partners with colleagues across product, engineering, policy, and legal to curb abuse and fraud at scale. You will work on projects that have global reach, with the aim of ensuring the safety and trust of Google’s products for users around the world.

The team places emphasis on delivering bold safety solutions for AI, continuously improving protection measures, and communicating findings to both technical and non-technical audiences. A willingness to handle sensitive content is part of the job, and the environment requires strong collaboration across a diverse set of stakeholders.

Advice for applicants

Highlight in your resume positions where you led data-heavy projects that combined ML design with deployment and evaluation, especially those tied to safety or fraud prevention. Use concrete numbers to show the impact of your work, like how your analysis changed a process or reduced risk.

When describing your experience, feature examples that demonstrate your five-year track record in data analysis, project leadership, and ML tooling, with specifics on Python and SQL usage and on any deployment or debugging you conducted in production environments.

Be prepared to walk through cross-functional collaborations with product managers, engineers, policy experts, and legal teams. Have a story ready about how you gathered requirements, translated model outputs for non-technical stakeholders, and iterated on a solution based on feedback and metrics.

Ask a precise question about how the team measures success in mitigating egregious harms and how they prioritise work across different products. A smart follow-up could focus on how first-quarter goals align with broader safety objectives and what a successful early milestone looks like for this role.