Senior Product Manager, AI Ecosystems, Frontier AI, DeepMind

Minimum qualifications

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

Preferred qualifications

• Experience building AI products, open-source developer platforms, SDKs, or model ecosystems rooted in significant model, algorithmic, or systems advancements.
• Understanding of the current 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 (such as planning, tool use, memory, and environment interaction).
• Ability to stay up-to-speed on all relevant research in open-weight foundation models, domain-specialized variants, interactive RL environments, multi-agent simulation, and agent harnesses.

About the job

As a Product Manager at DeepMind, you will be responsible for shipping AI research breakthroughs into Google’s AI models, products, and open ecosystems. As a Product Manager on Frontier AI within DeepMind, you will be working with AI researchers who are focused on making advances across new model architectures, AGI, and applications like Agents and Robotics. As the Product Manager for AI Ecosystems within Frontier AI, your job will be to guide the strategy and execution across our open-weight models specialized variants (such as the broader Gemmaverse ecosystem) as well as our open interactive training environments, agent harnesses, and privacy-preserving evaluation sandboxes.

You will understand how frontier research breakthroughs can be translated into ecosystems of open AI systems and creating an impactful two-way flywheel between the global open-source community and Google’s core foundation models like Gemini and Gemma. You will help research teams navigate technical, safety, and ecosystem tradeoffs, build collaborations with core model development teams, and partner with Google product areas (such as Cloud, Android, Pixel, and Search) as well as external developers, enterprises, academic researchers, and public-sector institutions to address real-world needs across on-device AI, sovereign and regulated workflows, scientific discovery, and societal benefit. In addition to shipping open-weight model variants, training recipes, and interactive environments, you will build the developer tooling, open-source runtime integrations, and communication strategy so that our open AI systems achieve widespread adoption and set the industry standard.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google 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.

France: €130000 - €133000 (EUR) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

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

• Understand the implications of new foundational research breakthroughs—across model architectures, multimodality, on-device efficiency, reinforcement learning, interactive environments, and trainable agent harnesses—on our open-weight model family (Gemma variants) and the broader AI ecosystem.
• Build collaborations with research teams, core model development teams, and product teams to ship open-weight model variants, post-training and mid-training environment runtimes, and modular agent harnesses—creating a two-way flywheel where open-source ecosystem innovations directly accelerate internal model training and product capabilities.
• Work with Google product areas and external partners to understand customer and societal needs that are best met through customizable open-weight models, on-device deployment, and zero-data-leakage evaluation and regulatory sandboxes.
• Build a thriving open ecosystem of developer tooling, standardized environment SDKs, formal verification and safety guardrails, and day-one integrations with popular open-source frameworks, agent protocols, and inference runtimes.