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Google Looking for Product Manager, GKE Generative AI Workloads, Google Cloud at Seattle, WA, USA Full Time
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Seattle, WA, USA; Kirkland, WA, USA.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience in product management, co-founder, or related technical role.
- 2 years of experience building and shipping technical products.
- Experience developing or launching products/technologies within Cloud, SaaS, enterprise, internal tools, or supply chain networks.
Preferred qualifications:
- Deep understanding of artificial intelligence, generative AI/GenAI workloads, AI training, AI inference, AI infrastructure, MLOps, NVIDIA GPUs, or TPUs.
- Knowledge of Kubernetes, Containers, and Cloud Native ecosystem.
- Ability to influence multiple stakeholders without direct authority.
- Excellent product management skills, including the ability to understand customer goals and pain points and craft elegant, innovative, and highly differentiated solutions.
- Excellent communication and presentation skills.
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.
Google Kubernetes Engine (GKE) is one of the most scalable and fully automated Kubernetes services available from public cloud providers, enabling developers and businesses to run their containerized applications on Google Cloud. As organizations everywhere rapidly adopt AI, GKE has nearly limitless potential for growth and impact. The GKE AI team is focused on making GKE an exceptional place for customers to run their AI workloads.
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.
The US base salary range for this full-time position is $139,000-$208,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Work closely with Google Cloud’s key AI customers who are building their AI platforms on Google Cloud’s infrastructure-as-a-service (IaaS) in order to understand customer needs and address them.
- Drive improvements in the Kubernetes open source community that address the needs of GKE AI customers.
- Work with our Kubernetes OSS team to define the strategy, priorities, and roadmap as well as to execute on them.
- Address critical barriers that hinder customers from using GKE to serve AI models, especially the inability to obtain GPU or TPU capacity without purchasing a long-term commitment, which is not cost effective for inference.
