Senior Staff Software Engineer, AI/ML AI Data

Senior Staff Software Engineer, AI/ML AI Data Share Senior Staff Software Engineer, AI/ML AI Data _corporate_fare_ Google _place_ Mountain View, CA, USA **Advanced** Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain. Share Senior Staff Software Engineer, AI/ML AI Data _info_outline_ XIn most instances, this position requires in-person interviews as part of the hiring process. **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 end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). + 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. + 5 years of experience with design and architecture; and testing/launching software products. **Preferred qualifications:** + Master’s degree or PhD in Engineering, Computer Science, or a related technical field. + 8 years of experience with data structures and algorithms. + 5 years of experience in a technical leadership role leading project teams and setting technical direction. + 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects. **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. The AI Data organization is responsible for democratizing high-quality AI/ML data assets and infrastructure to enable Google to rapidly and iteratively deliver safe, innovative, and impactful product experiences powered by AI models. We build scalable and automated infrastructure to manage ML assets at Google from development to launch with full traceability and auditability, without compromising dev velocity. 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: $262000 - $364000 (USD) + 25% bonus target + equity + benefits Learn more aboutbenefits at Google (https://www.google.com/about/careers/applications/benefits/) . **Responsibilities** + Understand the design and architecture of highly complex ML pipelines and distributed systems, with a view towards making them more resource-efficient while improving the quality of the AI/ML data assets they produce. + Drive the adoption of next-generation AI/ML techniques to solve ambiguous business problems, moving from conceptual prototypes to global production. + Set the "gold standard" for code quality, testing, and deployment within the team, performing architectural reviews and optimizing system performance. + Mentor executive and mid-level engineers, fostering a culture of technical growth, and represent the team in high-level technical discussions across the broader organization. + Partner with researchers to operationalize models and work with product stakeholders to align technical roadmaps with user needs. 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