Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 8 years of experience in product management or related technical role.
- 3 years of experience taking technical products from conception to launch (e.g., ideation to execution, end-to-end, 0 to 1, etc).
Preferred qualifications:- 5 years of experience working cross-functionally with engineering, research, and other cross functional partners.
- Experience with foundational models and LLMs.
About the jobAs a Product Manager for Gemini Modeling for Internationalization, you will own the core modeling roadmap to make Gemini a global engine. You will bridge the gap between research in pre-training, post-training, and reinforcement learning (RL) and consumer needs. You will drive the quality, evaluations, and data requirements for all internationalization modeling efforts, bridging core modalities and key product surfaces. This includes establishing a brand-new, scalable cross-lingual transfer playbook, driving upcoming launches for global activations, and shaping the unified helpfulness for Internationalization model-level evaluations. You will be highly technical and investigative, working directly with research and engineering teams to push the limits of what global AI can achieve.
The US base salary range for this full-time position is $256,000-$278,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .
Responsibilities- Define next-generation internationalization evaluation suites to assess global model performance across various modalities (agentic coding, multimodality). Build evaluations for linguistic/cultural relevance and helpfulness.
- Re-envision cross-lingual transfer by moving from pre-training modifications to a repeatable post-training and reinforcement learning playbook. Automate scaling across major languages using synthetic and real-world data.
- Optimize user experiences for specific regional markets by developing and iterating on localized prototypes. Ensure consistent language adherence across all consumer applications.
- Drive model readiness for key global distribution launches for first-party and third-party products.
