Research Engineer, Gemini Personalization Research, DeepMind

At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
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: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about
benefits at Google
.

Minimum qualifications
Bachelor’s degree or equivalent practical experience.
5 years of experience designing, constructing, and deploying evaluation frameworks that capture programmatic or algorithmic performance.

Preferred qualifications
1 year of agentic memory research experience.
Experience training and fine-tuning models in environments with accelerators, with a track record of deploying production-grade AI agents that manage tool-use (function calling) and complex state management.

Responsibilities
Drive research on post-training techniques (e.g., RL, SFT, and preference optimization) specifically tailored for personalization scenarios.
Develop novel evaluation frameworks to measure model quality against user behaviors/feedback.
Design and train agents capable of orchestrating tools and APIs to deliver hyper-personalized experiences.