Research Scientist, Machine Learning, Google

Key Skills

Generative AI, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP)

Job Description

Minimum qualifications:

• Master's degree in Computer Science or equivalent practical experience.
• Experience in Natural Language Processing (NLP) research.
• Ability to speak and write in English fluently.

Preferred qualifications:

• PhD in Natural Language Processing (NLP) or related field.
• Experience and or demonstrated passion for the AI space across Large Language Models, Generative AI, RPA, etc.
• Experience building production quality models (e.g., coding, testing, and evaluation).
• Experience in developing machine learning models.

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.

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

• Research and develop models for measuring consistency and alignment in multimodal generation, and their application to evaluation, training, and inference of such models.
• Explore novel model approaches and techniques, and be able to develop all parts of the components for modeling (e.g., data collection, evaluation setting, model training, testing, and inference).
• Collaborate with other research teams to expand output verification technology.