Product Manager, AI Training Data

MINIMUM QUALIFICATIONS: * Bachelor's degree or equivalent practical experience. * 3 years of experience in product management or a related technical role. * 1 year of experience taking technical products from conception to launch (e.g., ideation to execution, end-to-end, 0 to 1, etc). * Experience integrating generative AI tools or LLM interfaces into workflows. * Experience building developer platforms, developer-facing APIs/SDKs, or general backend infrastructure (e.g., for search or AI applications). PREFERRED QUALIFICATIONS: * Master's degree in a technology or business related field. * 1 year of experience in software development or engineering. * Experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and vector search. * Experience with tests of delivering AI solutions compliant with legal and safety guidelines. * Investigative skills with the ability to define user journeys and success metrics. ABOUT THE JOB: At Google, we put our users first. The world is always changing, so we need Product Managers who are continuously adapting and excited to work on products that affect millions of people every day. In this role, you will work cross-functionally to guide products from conception to launch by connecting the technical and business worlds. You can break down complex problems into steps that drive product development. One of the many reasons Google consistently brings innovative, world-changing products to market is because of the collaborative work we do in Product Management. Our team works closely with creative engineers, designers, marketers, etc. to help design and develop technologies that improve access to the world's information. We're responsible for guiding products throughout the execution cycle, focusing specifically on analyzing, positioning, packaging, promoting, and tailoring our solutions to our users. AI models require massive, compliant, high-quality data to unlock next-generation reasoning and agentic capabilities. In this role, you will partner with engineering, legal, and research leads across Google DeepMind to drive the product roadmap for AI training data infrastructure. You will oversee the complete pre-training and post-training data life-cycle, spanning automated deep-web acquisition, compliance filtering, and semantic dataset discovery. By optimizing pipeline cycle times and building specialized curation systems for key verticals like Code and Multimodal data, you will deliver, compliant platforms that accelerate model development and power Google’s AI future. The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $138000 - $197000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google [https://www.google.com/about/careers/applications/benefits/]. RESPONSIBILITIES: * Partner with research and product teams across DeepMind, Research, and Core to deeply understand their evolving training data needs and deliver high-quality, compliance-vetted datasets for next-generation models. * Drive the execution and roadmap for core components of the training data stack, translating research requirements into concrete features for data acquisition, trust and safety gating, or data curation systems. * Conduct internal customer and platform research to surface researcher pain points, data quality gaps, and emerging requirements in multimodal and agentic training datasets. * Define, track and optimize key success metrics for training data volume, processing pipeline velocity, and safety compliance standards.