• PhD in Machine Learning or a related field.
  
• Experience with training and optimizing large language models.
  
• Ability to grow under pressure in high-stakes, high-visibility environments.
  

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.

Our team is working on improving the accuracy and factuality of responses generated by Gemini, both standalone and in Retrieval Augmented Generation (RAG) settings. We iterate on the quality of RAG backends, and we build state-of-the-art language models through a variety of model training and optimization methods (e.g., Supervised Fine Tuning, Reinforcement Learning from Human Feedback). The team is also responsible for managing data quality, quality evaluation and quality monitoring.

Gemini is a conversational AI tool that enables users to collaborate with generative AI and help augment their imagination, expand their curiosity, and enhance their productivity.

Responsibilities

• Leverage model training strategies like Supervised Fine Tuning (SFT) and preference-based approaches (e.g., Reinforcement Learning from Human Feedback, Identity Preference Optimization, etc.) to improve the quality of language models.
  
• Iterate on the Retrieval-Augmented-Generation (RAG) approaches to improve the accuracy of language model responses.
  
• Collaborate with cross-functional and Research teams on applying state-of-the-art machine learning approaches to LLM-based products.