Senior Product Manager, Personalization Modeling, DeepMind

Senior Product Manager, Personalization Modeling, DeepMind Share Senior Product Manager, Personalization Modeling, DeepMind

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corporate_fareDeepMindplaceMountain View, CA, USA

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Share Senior Product Manager, Personalization Modeling, DeepMind

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Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of product management experience, with a focus on foundation models, LLM-based agents, personalization, or user modeling systems.

Preferred qualifications:

• Experience building evaluation frameworks or training data strategy.
• Experience designing simulated environments or synthetic data programs for machine learning, including with data vendors.
• Experience building personalization, memory, context retrieval, or user modeling systems at scale (e.g., recommendations, ranking, or user context understanding).
• Experience working on AI agentic products.
• Familiarity with privacy-preserving ML, data governance, and responsible AI practices for models that use personal data.
• Demonstrated ability to independently drive complex initiatives forward and influence research and engineering teams in ambiguous, rapidly evolving environments.

About the job Personalization is one of the biggest opportunities for agents. The most valuable tasks people delegate, at work and at home, depend on knowing who they are for: their preferences, history, relationships, and intent. As Product Manager lead for Gemini Personalization Modeling, you will own the core model capabilities that enable agents to understand and act on each user or team's context, preferences, and goals. You will improve Gemini models so that agents and products built on them can remember relevant context, infer intent, make tailored decisions, and take the right actions on a user's behalf across the web, apps, services, and devices. You will sit at the intersection of research and product, turning real-world agentic use cases into clear modeling requirements, evaluations, training data and environment strategies that measurably improve model quality. You will be the voice of users and bring personalized agentic capabilities to consumers, developers, and enterprises in a way that is privacy-preserving and secure by design, while keeping users in control of their data. 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.

US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google. Responsibilities

• Drive the vision, strategy, and roadmap for personalization in Gemini models. Engage with consumers, developers, and enterprise customers, and track competitive and market intelligence to identify high-value use cases and capability gaps.
• Establish and scale human and synthetic data, simulated environments, user personas, and user simulators needed to evaluate and train personalized behaviors, along with programmatic quality control that ensures a high yield of high quality data.
• Own the quality bar for personalization/personalized agent task success by defining the evaluations, benchmarks, autoraters, and human-rater programs that measure it, and use them to drive model hill-climbing, checkpoint selection, and release decisions. Work with Privacy, Trust & Safety, Security, and Responsible AI teams.
• Partner with post-training teams to set capability targets and close quality gaps/integration surfaces.

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