Group Product Manager, Relevance, GeminiApp, DeepMind

Google•Mountain View, CA

2d•$240,000 - $333,000

About The Position

As the Product Manager for the Gemini Relevance Platform, you will define and execute the product strategy for the machine learning engine powering discovery, personalization, and proactive engagement across the Gemini App. You will lead a team of ML engineers and data scientists working to make Gemini a daily habit for users worldwide. The role sits at the intersection of applied machine learning, systems architecture, and product UX. You will own the roadmap for moving Gemini from heuristic, random zero-state discovery to personalized, intent-aware experiences across in-app entry points and proactive out-of-app notifications. Google DeepMind is an interdisciplinary AI lab focused on advancing AI to address complex global challenges and support product innovation. The company emphasizes widespread public benefit, scientific discovery, safety, and ethics. Individual pay is based on factors including job-related skills, experience, and relevant education or training. The US salary range is $240,000–$333,000, with a 25% bonus target, equity, and benefits.

Requirements

Bachelor’s degree in Computer Science or equivalent practical experience.
10 years of experience in product management or a related role.
Experience as a Product Manager managing 0-to-1 recommendation systems, ML personalization platforms, or feed/discovery ranking products.

Nice To Haves

Deep technical fluency with modern ML architectures, feature-store infrastructure, offline/online training workflows, and low-latency real-time serving constraints.
Exceptional cross-functional leadership across Engineering, Data Science, UX, Legal/Privacy, and partner product organizations.
A proven track record driving growth and engagement metrics such as DAU, CTR, and session quality through rigorous A/B experimentation and causal modeling.

Responsibilities

Define the multi-quarter strategy and roadmap for evolving Gemini’s recommendation engine from rules-based ranking to deep ranking and retrieval models.
Lead product development for zero-state prompt discovery, contextual suggestions, session-resume modules (“Pick Up Where You Left Off”), and proactive daily routines.
Partner with Growth and Notification Platform teams to develop ML-driven targeting, personalized delivery windows, and lifecycle campaigns that increase daily active users.
Collaborate with Search, YouTube, Workspace, and other Google teams to use ecosystem signals, knowledge graph entities, and semantic embeddings safely and in compliance with privacy requirements.
Establish a metrics hierarchy that balances short-term click engagement (CTR) with conversation depth, response quality, and long-term user persistence.