Technical Lead Manager, Trusted Partner Cloud AI Operations

Set and communicate team priorities that support the broader organization's goals. Align strategy, processes, and decision-making across teams.Set clear expectations with individuals based on their level and role and aligned to the broader organization's goals. Meet regularly with individuals to discuss performance and development and provide feedback and coaching.Develop the mid-term technical direction and roadmap within the scope of your often multiple teams. Evolve the roadmap to meet anticipated future requirements and infrastructure needs.Design, guide and vet systems designs within the scope of the broader area, and write product or system development code to solve ambiguous problems.Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.Minimum qualifications:Bachelor’s degree or equivalent practical experience.8 years of experience in software development.5 years of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), or specialization in another ML field.3 years of experience in a technical leadership role.2 years of experience in a people management or team leadership role.2 years of experience leading to ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).Preferred qualifications:Master’s degree or PhD in Engineering, Computer Science, or a related technical field.3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.Experience scaling service operations.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.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: $207000 - $300000 (USD) + 20% bonus target + equity + benefitsLearn more about benefits at Google.Bachelor’s degree or equivalent practical experience.8 years of experience in software development.5 years of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), or specialization in another ML field.3 years of experience in a technical leadership role.2 years of experience in a people management or team leadership role.2 years of experience leading to ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).