Post-Training Technical Program Manager, Data Governance, DeepMind
DeepMind· enterprise· Artificial Intelligence / Research· Oct 19, 2026 · 9d ago
Mountain View, CA, USA
In office
Full-time
Staff
$147K–$218K est.
8+ years exp
Job description
Google's projects, like our users, span the globe and require managers to keep the big picture in focus while being able to dive into the unique engineering challenges we face daily. As a Technical Program Manager at Google, you lead complex, multi-disciplinary engineering projects using your engineering expertise. You plan requirements with internal customers and usher projects through the entire project lifecycle. This includes managing project schedules, identifying risks and clearly communicating them to project stakeholders. You're equally at home explaining your team's analyses and recommendations to executives as you are discussing the technical trade-offs in product development with engineers.
Using your extensive technical and leadership expertise, you manage projects of various size and scope, identifying future opportunities, improving processes and driving the technical directions of your programs.
Sit within the GenAI programs, this role is part of the post-training programs team. We enable our research and product teams to make rapid progress towards our mission, leading operational design and execution across the post-training area, with a focus on delivering the models and research innovations.
As the Technical Program Manager for Data Governance, you will own end-to-end training data compliance, dataset lineage automation, model and data card execution, and cross-functional release gating ensuring our models ship rapidly across first-party (1P) and third-party (3P) surfaces while meeting internal legal, privacy, and safety policies as well as external regulatory requirements.
Artificial intelligence will be one of humanity’s most transformative inventions. At DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google .
• Scope and lead data governance and compliance execution across Gemini post-training mixtures, ensuring adherence to internal policies and regulatory frameworks.
• Partner with research and data engineering teams to drive dataset cataloging, provenance tracking, and automated compliance verification.
• Collaborate with Research, Engineering, Safety, Privacy, and Legal teams to investigate data filtering alerts, identify root causes, and implement mitigations.
• Develop automated workflows and diagnostic tools to audit dataset lineage and streamline compliance reviews.
• Manage model release readiness across product and API surfaces, and lead the creation of model cards and data cards.
• Bachelor's degree in a technical field or equivalent practical experience.
• 8 years of experience in program management.
Minimum qualifications:
• Bachelor's degree in a technical field or equivalent practical experience.
• 8 years of experience in program management.
Preferred qualifications:
• 8 years of experience managing cross-functional or cross-team projects.
• Experience translating legal, privacy, safety, or regulatory policy requirements into actionable engineering workflows and technical controls.
• Experience in technical program management, data engineering, or ML systems, leading complex technical programs.
• Demonstrated ability to thrive in ambiguous research environments, aligning teams without direct authority.
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