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XIn accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:- Health, dental, vision, life, disability insurance
- Retirement Benefits: 401(k) with company match
- Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
- Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
- Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
- Baby Bonding Leave: 18 weeks
- Holidays: 13 paid days per year
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Seattle, WA, USA; Atlanta, GA, USA; Austin, TX, USA; Kirkland, WA, USA; Washington D.C., DC, USA.Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 5 years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
- 5 years of experience in the anti-abuse domain, with a focus on signal analysis, model development, data analysis, and cyber security.
- Experience programming in one or more languages (e.g., SQL, Python, or Golang).
Preferred qualifications:
- Experience performing fraud/abuse investigations, risk management, security, and threat analysis for communication products.
- Technical and product experience with end-to-end encrypted communication products and anti-abuse messaging solutions.
- Proficiency in large-scale data analysis, tools, and processes for building ML reputation models.
- Strong problem-solving, critical thinking, and communication skills with the ability to influence cross-functional stakeholders and senior leadership.
- Development of policy and enforcement models focused on actor and behavior-level strategies to identify and prevent harmful patterns, and privacy and safety-focused product design protections.
About the job
At Google, we work hard to earn our users’ trust every day. Our team’s mission in Trust and Safety is to partner with model and product teams to deliver trusted and helpful generative AI experiences with velocity. As deep experts in the trust and safety and responsible AI space, we deliver on our mission by defining product safety strategy, providing holistic insights on risks and opportunities and analyzing mitigations to drive strategic decisions. We serve as the Trust and Safety “front door” for product and model teams to coordinate cross-functional expertise and inputs to achieve fast, impactful results.
As a Senior Engineering Analyst/Tech Lead within Trust and Safety SAGE, you will lead the architecture and scaling of automated safety evaluation and enforcement platforms powering next-generation generative AI products. You will own the transition from bespoke evaluation scripts to high-throughput, low-latency LLM-as-a-judge autorater infrastructure supporting continuous offline testing and real-time launch decisions. Your role demands both deep technical acumen and a strong passion for GenAI integrity and AI abuse.
In this role, you will act as a leader across critical, high-scrutiny domains—including Health (YMYL clinical accuracy), Civics and Elections (misinformation and electoral integrity), and AI Ecosystem —ensuring our models operate with authoritative consensus, robust safety guardrails, and sub-100ms enforcement mitigations.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $159000 - $230000 (USD) + 15% bonus target + equity + benefits
Learn more about
benefits at Google.
Responsibilities
- Help to establish metrics, benchmarking Generative-AI issues and quantify improvements. Develop datasets and run evals for engineers to evaluate and improve Search, Labs and Assistant products. This role works with sensitive content and may be exposed to graphic, controversial, or upsetting content.
- Use technical judgment to drive project delivery, challenge proposals and identify risks. Manage interdependent projects of moderate to high complexity against crashed timelines.
- Improve tools through data analysis, technical expertise, and presentations to key stakeholders. Enhance operational workflows via process improvements and identification of automation opportunities.
- Design, develop, and scale production-grade LLM-as-a-judge autoraters and automated evaluation systems to run real-time launch decisions and continuous regression testing across core verticals.
- Partner directly with Product, Search Core, and GDM engineering teams to drive alignment on technical roadmaps, integrations and cross-product risk registers.
