Senior Product Manager, AI Ecosystems, Frontier AI, DeepMind

Google•Zurich, IDF

7d•$192,000 - $278,000

About The Position

As a Product Manager at Google DeepMind, you will ship AI research breakthroughs into Google’s AI models, products, and open ecosystems. On the Frontier AI team, you will work with researchers advancing model architectures, AGI, and applications such as agents and robotics. You will guide strategy and execution for open-weight models and specialized variants, interactive training environments, agent harnesses, and privacy-preserving evaluation sandboxes. The role connects frontier research with open AI ecosystems and builds a two-way flywheel between the global open-source community and Google’s foundation models, including Gemini and Gemma. You will help research teams navigate technical, safety, and ecosystem tradeoffs; collaborate with model-development and Google product teams; and partner with external developers, enterprises, academic researchers, and public-sector institutions. The work addresses needs including on-device AI, sovereign and regulated workflows, scientific discovery, and societal benefit. You will ship open-weight model variants, training recipes, and interactive environments, while building developer tooling, open-source runtime integrations, and communications strategies to drive adoption and help set industry standards. Safety and ethics are stated as priorities at Google DeepMind. The listed compensation is €130,000–€133,000 in France, plus a 20% bonus target, equity, and benefits; or $192,000–$278,000 in the US, plus a 20% bonus target, equity, and benefits. Pay depends on job-related skills, experience, and relevant education or training.

Requirements

Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
8 years of experience building AI products, open-source developer platforms, SDKs, or model ecosystems rooted in significant model, algorithmic, or systems advancements.

Nice To Haves

Experience building AI products, open-source developer platforms, SDKs, or model ecosystems rooted in significant model, algorithmic, or systems advancements.
Understanding of the open-weight model and agent landscape, including on-device and cloud inference stacks, fine-tuning and RL post-training workflows, procedural curricula, formal verification, and open-source developer tooling.
Deep understanding of AI foundation models, reinforcement learning, post-training techniques, and autonomous agent systems, including planning, tool use, memory, and environment interaction.
Ability to stay up to date on research in open-weight foundation models, domain-specialized variants, interactive RL environments, multi-agent simulation, and agent harnesses.

Responsibilities

Understand how foundational research breakthroughs in model architectures, multimodality, on-device efficiency, reinforcement learning, interactive environments, and trainable agent harnesses affect the Gemma model family and broader AI ecosystem.
Collaborate with research, core model-development, and product teams to ship open-weight model variants, post-training and mid-training environment runtimes, and modular agent harnesses.
Create a two-way flywheel in which open-source ecosystem innovations accelerate internal model training and product capabilities.
Work with Google product areas and external partners to identify customer and societal needs suited to customizable open-weight models, on-device deployment, and zero-data-leakage evaluation and regulatory sandboxes.
Build an open ecosystem of developer tooling, standardized environment SDKs, formal verification and safety guardrails, and day-one integrations with open-source frameworks, agent protocols, and inference runtimes.
Guide strategy and execution for open-weight models and specialized variants, interactive training environments, agent harnesses, and privacy-preserving evaluation sandboxes.
Develop developer tooling, open-source runtime integrations, and communication strategy to support adoption of open AI systems.