Senior Software Engineering Manager, Machine Learning, Labs at Google

Minimum qualifications:Bachelor's degree or equivalent practical experience. 5 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript). 5 years of experience in a technical leadership role; overseeing projects, with 5 years of experience in a people management, supervision/team leadership role.5 years of experience with machine learning algorithms and tools (e.g., TensorFlow), or applied ML (e.g., deep learning, natural language processing).Preferred qualifications:Master’s degree or PhD in Engineering, Computer Science, or a related technical field.5 years of experience working in a complex, matrixed organization. About the job Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.With your extensive technical expertise you take initiative to independently design and implement new systems, designing, implementing, and testing multiple features with little or no direction from tech lead or manager. You collaborate with key stakeholders to determine future direction of work.Labs is a group focused on incubating early-stage efforts in support of Google’s mission to organize the world’s information and make it universally accessible and useful. Our team exists to help discover and create new ways to advance our core products through exploration and the application of new technologies. We work to build new solutions that have the potential to transform how users interact with Google. Our goal is to drive innovation by developing new Google products and capabilities that deliver significant impact over longer timeframes.The US base salary range for this full-time position is $237,000-$337,000 + 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 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 long-term technical vision and roadmap within, and often beyond, the scope of your teams. Evolve the roadmap to meet anticipated future requirements and infrastructure needs. Oversee systems designs within the scope of the broader area, and review product or system development code to solve ambiguous problems. Review code developed by other engineers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).