Technical Program Manager, End-to-End AI Systems Integration, DeepMind

Technical Program Manager, End-to-End AI Systems Integration, DeepMind Share Technical Program Manager, End-to-End AI Systems Integration, DeepMind

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corporate_fareDeepMindplaceMountain View, CA, USA

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Share Technical Program Manager, End-to-End AI Systems Integration, DeepMind

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Minimum qualifications:

• Bachelor's degree in a technical field, or equivalent practical experience.
• 8 years of experience in program management.
• Experience in technical program management, artificial intelligence, and software development.

Preferred qualifications:

• 8 years of experience managing cross-functional or cross-team projects.
• Experience with production AI systems, including modern Large Language Model (LLM) training lifecycles, inference economics, and Site Reliability Engineering (SRE) practices.
• Experience managing AI software launches through security, privacy, legal, and safety review processes.
• Experience synthesizing research breakthroughs and evaluation metrics into roadmaps, architectural trade-offs, and executive decision frameworks.
• Experience with AI platforms, evaluations, and Software Development Life Cycle (SDLC) management.

About the job Delivering next-generation AI experiences requires more than a breakthrough model it requires seamless end-to-end integration across frontier models, agentic orchestration layers (runtime harnesses, tool-use, memory, and grounding systems), and user-facing product surfaces. As a Technical Program Manager for End-to-End AI Systems Integration, you will own the technical execution and cross-functional orchestration that brings DeepMind’s latest reasoning, multimodal, and agentic capabilities from research into production at global scale. You will operate at the critical intersection of model development, system architecture, evaluation science, and product delivery ensuring that models, runtime harnesses, serving infrastructure, and product experiences are co-designed, rigorously evaluated, and launched safely and reliably. You will partner deeply across the entire lifecycle, such as with Research Scientists, Infrastructure Engineers, Site Reliability Engineering (SRE), Product Management (PM), User Experience (UX), and governance partners across Legal, Privacy, Security, and Trust and Safety to resolve complex technical dependencies, establish continuous evaluation feedback loops, and ship high-impact AI products. 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. Responsibilities

• Drive the end-to-end technical integration and delivery of AI capabilities across the full stack, connecting foundation models, agentic runtime harnesses, serving infrastructure, and product surfaces.
• Partner with research and engineering teams to design, automate, and operationalize comprehensive evaluation pipelines, channeling real-world product telemetry back into model iteration.
• Coordinate execution across a matrix of stakeholders to align system architectures, API contracts, latency/compute budgets, and release milestones.
• Partner with Infrastructure and Site Reliability Engineering (SRE) teams to manage serving economics, establish production Service Level Objectives (SLOs), and build resilient deployment workflows.
• Navigate complex launch readiness and compliance gates, driving red-teaming, adversarial robustness testing, data governance reviews, and safety alignment sign-offs.

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