Google Research is seeking a Senior Research Scientist to join their Interactive Recommender Systems team. This role combines cutting-edge research with practical applications, focusing on developing next-generation technologies for Google's systems and user-facing products. The position requires expertise in multiple areas including recommender systems, machine learning, and multi-agent systems.
The role offers a unique opportunity to work at the intersection of fundamental research and product development, with the freedom to pursue innovative ideas while making real-world impact. You'll be contributing to systems that affect billions of users worldwide, while also having the opportunity to publish your findings and collaborate with academic institutions.
As a Senior Research Scientist, you'll be working on complex challenges in interactive systems, conversation recommender systems, and generative models. The role involves developing and implementing scalable machine learning solutions, with a particular focus on long-term user satisfaction in complex ecosystems. You'll be part of a collaborative team that values both research excellence and practical implementation.
The position offers competitive compensation ($161,000-$239,000) plus bonus, equity, and comprehensive benefits. Google's commitment to research innovation, coupled with its global reach and impact, makes this an exceptional opportunity for researchers who want to push the boundaries of technology while seeing their work deployed at scale.
The ideal candidate will have a PhD in Computer Science or related field, strong research experience, and a track record of publications. You'll need expertise in areas such as multi-agent systems, recommender systems, reinforcement learning, or generative AI. The role requires both technical depth and the ability to lead research initiatives while collaborating with cross-functional teams.
At Google Research, you'll be part of an organization that maintains a portfolio of research projects driven by fundamental research and product innovation. The team's culture encourages sharing research findings with the broader scientific community while working on problems that span the breadth of computer science.