Staff Software Engineer, AI/ML, ML Infrastructure, Search Ads

Staff Software Engineer, AI/ML, ML Infrastructure, Search Ads Share Staff Software Engineer, AI/ML, ML Infrastructure, Search Ads _corporate_fare_ Google _place_ New York, NY, USA **Advanced** Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain. Share Staff Software Engineer, AI/ML, ML Infrastructure, Search Ads _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 programming in Python or C++. + 5 years of experience leading ML design and optimizing end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). + 5 years of experience testing, and launching software products. + 3 years of experience with software design and architecture. + 2 years of experience with GenAI techniques (e.g., large language models, multi-modal, large vision models) or with GenAI-related concepts (e.g., language modeling, computer vision). **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. + 3 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. With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions. Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business. Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Learn more aboutbenefits at Google (https://www.google.com/about/careers/applications/benefits/) . **Responsibilities** + Work across many different aspects of machine learning infrastructure and modeling. + Support new modeling techniques and products for changing infrastructure (training and serving). Prototype new infrastructure, automation, workflows, and tooling. + Analyze and debug performance, and make improvements. Improve robustness, reliability, usability of infrastructure. + Collaborate closely with other members of our team and partners to build systems that take advantage of and integrate seamlessly across our ecosystem. Engage with modeling teams to explore pain points and novel use cases. + Research emerging technology and make trade-off decisions on immediate wins vs longer-term stability. 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