Minimum qualifications:
- 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).
- 3 years of experience with AI Platforms.
Preferred qualifications:
- Master's degree in a technology or business related field.
- Experience designing, launching, or scaling agentic runtime architectures, including execution environments, state and context management, and system routing frameworks.
- Experience designing, building, or launching platform products within secure, or air-gapped sovereign environments.
- Technical understanding of AI inference runtimes, model execution optimization, GPU hardware acceleration, or low-latency serving stacks.
- Familiarity with advanced ML inference optimization techniques (e.g., disaggregated serving, KV cache management, speculative decoding, and model quantization) to support system-level latency and throughput improvements.
- Track record of evaluating price-performance trade-offs, compute node allocation, and capacity planning for inference 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 help bring Google's AI capabilities to secure, regulated, and sovereign environments around the world. By supporting model hosting, optimized model execution, and agent runtimes, you will enable enterprise and public sector customers to deploy and scale AI capabilities on Google Distributed Cloud (GDC) with high standards of security, performance, and compliance.
You will help solve complex issues in AI: delivering Gemini models and agentic runtimes to secure sovereign clouds, where you will shape the future of AI for national security, global governments, and regulated enterprise industries.
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 - $279000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
In this role, you will help bring Google's AI capabilities to secure, regulated, and sovereign environments around the world. By supporting model hosting, optimized model execution, and agent runtimes, you will enable enterprise and public sector customers to deploy and scale AI capabilities on Google Distributed Cloud (GDC) with high standards of security, performance, and compliance.
You will help solve complex issues in AI: delivering Gemini models and agentic runtimes to secure sovereign clouds, where you will shape the future of AI for national security, global governments, and regulated enterprise industries.
Responsibilities
- Define the long-term goal, strategy, and roadmap for bringing AI capabilities, models, and developer platform tools to secure and sovereign cloud environments.
- Partner with engineering to manage the lifecycle of onboarding, deploying, and supporting first-party and third-party AI models to run on specialized secure infrastructure.
- Resolve performance bottlenecks, hardware resource allocation issues, and latency challenges to support performance in air-gapped environments.
- Work closely with key public sector and enterprise stakeholders to translate stringent security, safety guardrails, and compliance requirements into secure product designs.
- Collaborate with cross-company AI research, infrastructure, safety, and business teams to align technical roadmaps, ensure API compatibility, and streamline product execution.