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

Google•Mountain View, CA

2h•$192,000 - $278,000

About The Position

The Technical Program Manager for End-to-End AI Systems Integration owns technical execution and cross-functional orchestration to bring DeepMind’s reasoning, multimodal, and agentic capabilities from research into production at global scale. The role works across model development, system architecture, evaluation science, and product delivery to ensure models, runtime harnesses, serving infrastructure, and product experiences are co-designed, rigorously evaluated, and launched safely and reliably. The position partners across the lifecycle with Research Scientists, Infrastructure Engineers, Site Reliability Engineering, Product Management, User Experience, and governance teams including Legal, Privacy, Security, and Trust and Safety. It resolves technical dependencies, establishes continuous evaluation feedback loops, and supports the delivery of high-impact AI products. Individual pay is determined by job-related skills, experience, and relevant education or training. The stated US compensation is $192,000–$278,000, plus a 20% bonus target, equity, and benefits.

Requirements

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.

Nice To Haves

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.

Responsibilities

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