Research Scientist, Gemini Personal Intelligence

Mountain View, California, United StatesOn-site

Requirements

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact.
If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. About Us
Our team drives the Personal Intelligence research behind Gemini, with a mission to make AI more personal, proactive, and context-aware.
You will push the boundaries of Large Language Models (LLMs) to build the brain of the world’s most helpful personal assistant—one that securely integrates with users' personal data to solve real-world problems.
Agency: Empowering AI models to autonomously plan, use tools, and execute complex tasks on behalf of the user. The Role
As a Research Scientist for Gemini Personal Intelligence, you will advance the state-of-the-art in understanding and reasoning to create an AI that truly understands, remembers, and adapts to the user's unique life and context.
In order to set you up for success as a at Google DeepMind, we look for the following skills and experience:
PhD in Machine Learning, Computer Science, or a relevant field (or equivalent practical research experience).
experience with modern post-training methods (SFT, RLHF, etc.).

benefits we offer: enhanced maternity, paternity, adoption, and shared parental leave, private medical and dental insurance for yourself and any dependents, and flexible working options.
The US base salary range for this full-time position is between $141,000 USD - 244,000 USD + bonus + equity + benefits.
Your recruiter can share more about the specific salary range for your targeted location during the hiring process.

Personalization: Inferring and adapting to user intent, preferences, communication styles, etc.
Context: Reasoning over extensive personal history (e.g., Gmail, Photos, Drive) across diverse modalities (Text, Image, Audio, Video).
Driving research on post-training techniques (e.g., RL, SFT, and preference optimization) specifically tailored for personalization scenarios.