Staff AI Research Engineer, Large User Models

Staff AI Research Engineer, Large User Models Google Mountain View, CA, USA Advanced Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain. Apply Bachelor’s degree or equivalent practical experience. 8 years of experience in software development. 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture. 2 years of experience in research, leading multiple research efforts and influencing research direction related to Foundation Models, Large Language Models, etc. Experience with Transformer-based models (e.g., BERT, T5, GPT, ViT), attention mechanisms, and architectural variations. Preferred qualifications: Master’s degree or PhD in Engineering, Computer Science, or a related technical field. 8 years of experience with data structures/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. Demonstrated expertise in publications (e.g., NeurIPS, ICML, RecSys) or significant open-source contributions in RecSys, NLP, or Multimodal systems. 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 Staff AI Research Engineer, you will architect the strategy and roadmap for Foundation Recommender Model pre‑training. You will own the research agenda, defining and prioritizing experiments to drive continuous model quality within compute constraints. This is a collaborative role that requires partnership with data leads to shape collective roadmaps, ML infrastructure leads to define training framework requirements, and engagement teams to establish evaluation benchmarks. You will play a pivotal role in advancing state‑of‑the‑art recommendation capabilities from conception to model release. The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide. We’re the driving force behind Google’s groundbreaking innovations, empowering the development of our cutting‑edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world‑leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more. The US base salary range for this full‑time position is $197,000–$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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 Define and execute the long‑term strategy for Foundation Recommender Model pre‑training, encompassing both model architecture evolution and future training methodologies. Drive a high‑velocity research agenda focused on model quality, strategically prioritizing experiments based on compute capacity and researcher bandwidth. Partner with ML infrastructure teams to architect training frameworks and ensure the technical ecosystem supports the research and release roadmap. Collaborate with data teams to plan data collection for pre‑training, setting the standards for data quality and scale required to meet foundational model objectives. Establish robust evaluation benchmarks and maintain engaged leaderboards to track progress against baselines and ensure industry‑leading performance. Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents‑to‑be, criminal histories consistent with legal requirements, or any other basis protected by law. Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting. To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes. #J-18808-Ljbffr NLP PEOPLE