1 year of experience in a technical leadership role.

Experience with training and evaluating Large Language Models.

Experience contributing to Generative AI research, including publishing at conferences (e.g., NeurIPS, ICML).

Experience with collecting and analyzing data.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

The Gemini Factuality team works to measure and improve factuality in Gemini post-trained models. We collaborate with numerous research and engineering teams working on Gemini models and their applications. In this role, you will work on theoretical and applied research to deeply understand the relationships between factuality and other quality aspects. You will keep track of model development work that potentially impacts factuality and can inform the development roadmap. You will document and regularly present your work internally within the team, and to executive stakeholders.

Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.

Responsibilities

• Write and test product or system development code.
• Participate in or lead Gemini factuality improvements with peers and stakeholders.
• Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
• Participate in the full lifecycle of Machine Learning (ML) projects such as defining new desired model behaviors, creating metrics and human evals, collecting supervised fine-tuning and reward modeling data, training and evaluating models, and analyzing logs traffic to understand new error patterns.