Group Product Manager, Frontier Model Protection, DeepMind

MINIMUM QUALIFICATIONS: * Bachelor's degreeor equivalent practical experience. * 10 years of experience in product management or a related technical role. * 5 years of experience taking technical products from conception to launch (e.g., ideation to execution, end-to-end, 0 to 1, etc.). PREFERRED QUALIFICATIONS: * Master’s degree, MBA, or PhD in Computer Science, Machine Learning, Cybersecurity, Business, or a related field. * Direct domain experience in AI security, model defense, anti-distillation techniques, adversarial ML, and model extraction threat modeling. * Familiarity with emerging AI governance frameworks, LLM security standards (e.g., OWASP Top 10), and external safety coalitions. * Proven track record of collaborating directly with PhD-level researchers to transition novel algorithmic discoveries into low-latency, production-hardened systems. ABOUT THE JOB: 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. In this role, you will own the product strategy for Frontier Model Protection. As foundation models achieve reasoning and multimodal capabilities, safeguarding our innovations against model extraction, unauthorized distillation, and intellectual property theft is a priority for national security and safety. You will set the goal, governance, and defense architectures that keep our models resilient. The "product" is the model protection posture and runtime defense suite that safeguards Google DeepMind’s research assets while balancing business growth and developer innovation. You will lead through strategy, executive alignment, and measurement. You will define how to protect model intelligence, and own trade-offs between defense posture, utility, developer experience, and commercial momentum. You will represent Google DeepMind externally—shaping safety strategies with government stakeholders and peer labs to establish safety benchmarks and evaluation standards. Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority. We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $300000 - $333000 (USD) + 25% bonus target + equity + benefits Learn more about benefits at Google [https://www.google.com/about/careers/applications/benefits/]. RESPONSIBILITIES: * Define the multi-year product goal and strategic roadmap for frontier model protection and anti-distillation defenses across Google DeepMind. * Establish policies and runtime guardrails to balance rigorous IP defense against developer friction, model utility, and commercial growth objectives. * Develop quantitative evaluation frameworks and telemetry to measure defense efficacy and continuously validate complex trade-off decisions. * Partner with researchers and engineers to translate breakthroughs in watermarking and query analysis into globally scalable, production-grade defenses. * Secure executive alignment and lead technical engagement with peer labs and AI Safety Institutes to establish shared evaluation benchmarks.