Product Manager, Gemini API Models, DeepMind
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience
- 5 years of experience in Product Management, Developer Relations, or a technical role working on developer-facing products or AI systems.
- Experience building or integrating with Application Programming Interfaces (APIs).
- Experience working with Machine Learning (ML) models, training methodologies, or evaluation datasets.
Preferred qualifications:
- Experience working in a developer-facing role, including Developer Experience (DevX) or forward-deployed engineering.
- Experience working directly with Machine Learning (ML) research, model training teams, or evaluation systems.
- Experience establishing developer feedback loops, beta testing programs, or advisory councils.
- Experience improving developer experiences.
- Familiarity with the Gemini Application Programming Interface (API), Google AI Studio, or competing developer AI platforms.
- Familiarity with the wider Generative AI (GenAI) ecosystem and orchestration frameworks.
About the job
The Gemini API and Google AI Studio have grown to support millions of developers and process trillions of tokens, becoming a critical part of how many of Google customers access our latest AI models. As the pace of model development accelerates with new Gemini generations, new modalities, tools, and consumption tiers, the process of bringing these models to developers is becoming both more complex and more consequential. Every model launch is an opportunity to strengthen the developer relationship and make our next generation of models better.
As Product Manager, you will be the developer's advocate in the model development and release process, partnering closely with the Developer Experience (DevX) and Platform teams and multiple cross-functional Product Areas to ensure a unified release.
Working closely with API and research teams, you will own the end-to-end model lifecycle on the Gemini API and Google AI Studio core UI surface. Starting with validating model capabilities and the API surface during our Early Access Program, you will ensure every model ships with sensible defaults, excellent tooling support, clear documentation, and a strong GTM strategy. You will be accountable for successfully landing the model via driving post-launch adoption, ensuring long-term developer success, and delivering sustained business impact.
In this role, you will bring relentless developer empathy to model decisions, establish continuous developer feedback loops, and make sure developer signals shape what the research team builds next.
Success means developers love how Gemini models show up on our platform and we have a robust, real-time understanding of how our models perform in real-world developer workloads that influence modeling roadmap.
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.
US: $217000 - $237000 (USD) + 15% bonus target + bonus + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Manage the full model release lifecycle from Early Access Programs to General Availability and deprecation.
- Define operational release parameters, including pricing, Stock Keeping Units (SKUs), capacity planning, and usage limits.
- Partner with Developer Experience (DevX), Software Development Kit (SDK), and Marketing teams to drive post-launch model adoption. Partner with Google DeepMind (GDM) research and modeling teams to represent developer requirements and shape model roadmaps.
- Influence API surface design to ensure optimal developer workflows and high-quality integration documentation.
- Establish developer feedback loops to monitor production performance and build robust evaluation datasets.
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