About the job
As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
Responsibilities
Design modeling innovations to enable Gemini to autonomously acquire and reason over deep, structured domain knowledge.
Advance next-generation retrieval architectures and natively multimodal Gemini representations to improve intent capture and retrieval quality.
Build and train models that can autonomously reflect on outputs and self-improve through experience.
Collaborate with product teams like Search and YouTube to scale research innovations into production environments, optimizing for both quality and efficiency.
Qualifications
Minimum
Bachelor's degree in Computer Science, a relevant technical field with a focus on AI research, or equivalent practical experience.
8 years of experience in the full lifecycle of research modeling, with a specific emphasis on ensuring quality outputs and autonomous learning in AI domains.
Experience in developing and scaling Large Language Models (LLMs) or Information Retrieval (IR) systems.
Preferred
Knowledge of Reinforcement Learning (RL) or automated evaluation systems.
Ability to solve exceptionally complex problems (e.g., math and programming competitions or developing novel algorithms).
Ability to quickly prototype and iterate on complex systems.
Strong coding skills and a "hacker" mindset for implementing complex algorithms and multi-stage training pipelines.
Interest in the intersection of research and large-scale real-world applications.