Senior Product Manager, AI Model Safety, Google Cloud AI

At Google, we put our users first. The world is always changing, so we need Product Managers who are continuously adapting and excited to work on products that affect millions of people every day.

In this role, you will work cross-functionally to guide products from conception to launch by connecting the technical and business worlds. You can break down complex problems into steps that drive product development.

One of the many reasons Google consistently brings innovative, world-changing products to market is because of the collaborative work we do in Product Management. Our team works closely with creative engineers, designers, marketers, etc. to help design and develop technologies that improve access to the world's information. We're responsible for guiding products throughout the execution cycle, focusing specifically on analyzing, positioning, packaging, promoting, and tailoring our solutions to our users.

As a Senior Product Manager for AI Model Safety, you will lead the product strategy, roadmap, and execution for trust, safety, and abuse prevention across AI models served on Google Cloud Agent Platform. You will sit at the critical intersection of cutting-edge generative AI infrastructure, multi-tenant enterprise requirements, leading AI model providers, and Google’s rigorous Trust and Safety standards. You will define end-to-end safety architectures from sub-millisecond synchronous input/output guardrails and automated abuse detection to human review and regulatory compliance reporting.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

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

US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Define and drive the multi-year product strategy for AI model serving safety across Google Cloud’s Model-as-a-Service (MaaS) and Agent Platform ecosystems.
• Present low-latency, scalable safety capabilities for AI model inference, including real-time prompt/response filtering and abuse detection.
• Build enterprise-grade safety controls enabling custom threshold policies, Bring-Your-Own-Guardrails (BYOG), and telemetry without sacrificing inference performance.
• Partner with AI model providers to align safety standards, co-develop shared responsibility matrices, and establish SLAs.
• Orchestrate alignment across Engineering, Trust and Safety, Legal, Policy, and Research teams to architect automated abuse triage and manage regulatory escalation pipelines.

Minimum qualifications:

• Bachelor’s degree in Computer Science, Electrical Engineering, Mathematics, a related technical field, or equivalent practical experience.
• 8 years of experience in product management or equivalent technical leadership experience in cloud platforms, developer APIs, or machine learning infrastructure.
• 3 years of experience taking technical products from conception to launch.
• Experience in Trust and Safety, AI safety, content moderation, platform abuse mitigation, or enterprise cloud security and compliance.
• Experience integrating generative AI tools or Large Language Model (LLM) interfaces into workflows.

Preferred qualifications:

• MBA or Master’s degree in Computer Science, AI/ML, or a related technical or policy discipline.
• 5 years of product management experience shipping large-scale, high-availability developer platforms or AI/ML inference infrastructure.
• Experience with Generative AI safety architectures (e.g., LLM input/output moderation, perceptual hashing, RLHF).
• Experience managing third-party partner relationships, commercial co-development contracts, and joint technical roadmaps.
• Experience with high-throughput and low-latency inference systems, distributed service architectures, and latency-budget trade-offs.