Engineering Manager, GKE AI, Experience and Ecosystem
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
- 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience with one or more of the following: Speech/audio (technology duplicating and responding to the human voice), reinforcement learning (sequential decision making), ML infrastructure.
- 3 years of experience in a technical leadership role; overseeing projects, with 2 years of experience in a people management, supervision/team leadership role.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 5 years of experience in building distributed systems and applying ML.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
- Experience in implementing and managing MLOps solutions.
About the job
Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.
With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.
The Google Kubernetes Engine Artificial Intelligence (GKE) Artificial Intelligence (AI) Ecosystem aspires to transform Google Kubernetes Engine into the premier platform for AI/ML workloads by enabling seamless integration with open-source frameworks and Google Cloud's AI tools. The foundation of the goal is in nurturing a vibrant ecosystem of AIEco tools that lowers barriers to AI adoption, making GKE the default choice for scalable, reliable, secure and efficient AI deployments. The ultimate commitment is to position GKE as the leader in AI infrastructure, setting new standards for performance, interoperability, and community-driven innovation.
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 $189,000-$284,000 + 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 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
- Set and communicate team priorities that support the organization's goals. Align strategy, processes, and decision-making across teams.
- Set clear expectations with individuals based on the level and role and aligned to the organization's goals. Meet regularly with individuals to discuss performance and development and provide feedback and coaching.
- Develop the mid-term technical goal and roadmap within the scope of multiple teams. Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
- Lead the GKE interoperability experience strategy with other cloud services, like Vertex AI, expanding integrations to simplify AI/ML adoption.
- Oversee GKE AI Solutions platform, accelerating customer onboarding through Terraform-driven automation, Marketplace offerings and automated deployments.
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Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
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