Senior Product Manager, Personalization Modeling, DeepMind
- Mountain View, California
- Onsite
- full time
- $192k–$278k/year
- Posted today
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About the role
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
[https://www.google.com/about/careers/applications/benefits/].
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.
Skills
- Product Management
- Foundation Models
- LLM-based Agents
- Personalization
- User Modeling
- Evaluation Frameworks
- Training Data Strategy
- Synthetic Data
- Machine Learning
- Context Retrieval
- Privacy-preserving ML
- Data Governance
- Responsible AI
- Strategic Planning
- Stakeholder Management
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