Drive innovation as a Technical Program Manager specializing in AI Data. This role is crucial for advancing ML data operations, governance, and responsible practices across Google. You will work with a dedicated team focused on enhancing ML data infrastructure and supporting critical AI initiatives, including major LLM launches.

Our mission is to ensure high-quality data is readily accessible for all AI tasks, from human and synthetic data to licensed and proprietary Google data. You'll be instrumental in securing AI models with robust lineage tracking, transparent approval processes, and protected storage, thereby accelerating accountable product and model development.

Join a dynamic environment where you'll collaborate directly with model builders and product leadership. This position offers the opportunity to shape the future of AI by working on cutting-edge projects within a fast-paced, impactful, and collaborative setting.

Lead comprehensive program execution for tamper-proof data lineage, transparent approvals, and secure storage solutions. This includes overseeing initiatives related to safety risk evaluation and efficient data acquisition.

Foster strong collaboration with engineering, product management, and other key partners to meticulously define all workstreams, requirements, project schedules, resource allocation, and critical milestones.

Effectively communicate project status and insights to diverse audiences, ranging from cross-functional teams to executive leadership.

Champion feature planning and cultivate strong cohesion and alignment among all stakeholders.

Exhibit exceptional technical leadership and thought leadership to significantly enhance project efficiency and outcomes.

A Bachelor's degree in a technical field or equivalent practical experience is required.

We are seeking candidates with a minimum of 5 years of dedicated program management experience.

Preferred qualifications include 5 years of experience managing cross-functional or cross-team projects, alongside demonstrated experience with large-scale data management. Familiarity with quantitative analysis, cost-effectiveness assessment techniques, and AI model lifecycle processes such as training, testing, evaluation, and tuning is highly valued. A strong understanding of data quality metrics and KPIs relevant to AI products is also essential.