Senior Staff Software Engineer, AI/ML, Google Workspace

Design, develop, test, deploy, maintain, and enhance large scale software solutions.Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.Drive technical project strategy, lead large-scale ML infrastructure optimization, and oversee the design and implementation of solutions across multiple specialized ML areas.Minimum qualifications:Bachelor’s degree or equivalent practical experience.8 years of experience in software development.7 years of experience leading technical project strategy, ML design, and optimizing industry-scale end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).5 years of experience with design and architecture; and testing/launching software products.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), ML infrastructure, or specialization in another ML field.Preferred qualifications:Master’s degree or PhD in Engineering, Computer Science, or a related technical field.8 years of experience with data structures/algorithms.5 years of experience in a technical leadership role leading project teams and setting technical direction.3 years of experience working in an organization involving cross-functional, or cross-business projects.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.AI will change the future of work in profound ways, and our products— Gmail, Docs, Drive, Calendar, Sheets, Vids and Meet are at the forefront. From pre-computed summaries for email threads, summaries for meetings, and videos created from a document using lifelike AI avatars, our AI opportunity is huge. Our mission is to meaningfully connect people so they can create, build, and grow together and as part of the team you can build how productivity tools should work 5-10 years into the future. You will work with model builders (Google DeepMind), work with exceptional leaders, and have the ability to impact billions of users across the world.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $262000 - $364000 (USD) + 25% bonus target + equity + benefitsLearn more about benefits at Google.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; Mountain View, CA, USA.Bachelor’s degree or equivalent practical experience.8 years of experience in software development.7 years of experience leading technical project strategy, ML design, and optimizing industry-scale end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).5 years of experience with design and architecture; and testing/launching software products.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), ML infrastructure, or specialization in another ML field.