Senior Product Manager, GenAI Media Safety

About the role

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.

Responsibilities

• Build a cohesive product idea that aligns safety principles and capabilities with overall YouTube goals.
• Determine how to effectively implement safety-by-design into media generation pipelines (e.g., balancing prompt filtering, model guardrails, and output classification).
• Define/Develop evaluation protocols (red teaming, benchmarking) for feature deployment.
• Own and deliver upon the strategy for safety metrics (build the system).
• Drive execution towards key safety metrics.
• Build a safety platform that can sustainably grow/adapt while ensuring systems are set up to rapidly respond to escalations.
• Define product principles that prioritize long-term user health in the context of AI-generated media. Support this with testing, metrics, and responsibility systems to prevent harm.

Requirements

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in product management or related technical role.
• 3 years of experience taking technical products from conception to launch (e.g., ideation to execution, end-to-end, 0 to 1, etc.).
• 2 years of experience in GenAI Media (Image/Video/Audio) safety and guardrails.
• 2 years of experience building multi-modal safety stacks.

Preferred qualifications

• Experience with analytical approaches to model evaluations for media, including managing adversarial datasets (red teaming).
• Experience building digital experiences for minors.
• Familiarity with media generation models and applying guardrails to them.
• Demonstrated success in partnering with engineering to make high-stakes architectural decisions, such as weighing the latency, cost, and effectiveness of input prompts vs. output filters vs. model fine-tuning.
• Proven track record of shipping ML-driven safety features for visual or auditory content.

Benefits

Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $192000 - $279000 (USD) + 20% bonus target + equity + benefits