Technical Program Manager, Cross-Product Execution and Model Feedback Operations, DeepMind
A Technical Program Manager at Google DeepMind who builds and scales the operating model linking frontier AI research with Google's product divisions — managing cross-product AI initiative portfolios, executive prioritization reviews, and feedback loops that channel product insights and evaluation signals back into model development. Requires 8+ years of program management and AI/ML experience.
- Compensation
- $192K–$278K / yr
- Location
- Mountain View, CA, USA; San Bruno, CA, USA; San Francisco, CA, USA
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Project management
- Posted
Skills
Must have
- Bachelor's degree in a technical field, or equivalent practical experience
- 8 years of experience in program management
- Experience in technical program management, cross-functional collaboration, and software development
- Experience working with Artificial Intelligence or Machine Learning technologies
Nice to have
- 8 years of experience managing cross-functional or cross-team projects
- Experience with modern LLMs, multimodal models, and agentic systems
- Experience with model training, evaluation design, and inference economics (latency, cost, compute capacity)
- Experience building scalable operating models and repeatable processes across organizational boundaries
- Experience with AI platforms, data acquisition, and SDLC management
Full description
Artificial intelligence could be one of humanity’s most useful inventions. At DeepMind, we’re a team of scientists, engineers, machine learning experts, and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.
To turn Google’s product scale into a lasting advantage, DeepMind requires a strong, two-way bridge between our frontier AI research and Google’s global product ecosystems.
As a Technical Program Manager for Cross-Product Engagements, you will build and scale the operating model connecting DeepMind’s research teams with Google’s major product divisions. Stepping above day-to-day launch execution, you will focus on the broader strategic ecosystem helping product teams adopt frontier models and shared AI capabilities faster, while channeling real-world product insights and evaluation signals back into research. By connecting model advancements with long-term product roadmaps, you will ensure DeepMind and our product partners maintain a shared technical goal and seamless alignment across major company priorities.
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 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.
- Partner with executive leadership across DeepMind and Google’s product divisions to align frontier research roadmaps, product strategy, and shared multi-year goals.
- Define clear rules of engagement and manage a prioritized portfolio of cross-product AI initiatives, leading executive review forums focused on strategic prioritization, resourcing trade-offs, and cross-organizational alignment.
- Partner with product and research teams to connect real-world product insights with core research, establishing scalable frameworks for outcome-based evaluations and structured product feedback that inform future model development.