Google Accra

Minimum qualifications:

Bachelor's degree in Computer Science, a related field, or equivalent practical experience with computer science, data structures, algorithms, and software engineering.
Experience programming in one or more of the following languages: Python, C++.
Experience in Machine Learning and Natural Language Processing.

Preferred qualifications:

Experience delivering projects.
Research experience in cross-lingual transfer learning, few-shot learning, and/or zero-shot learning.
Familiarity with the latest developments and breakthroughs in NLP and low-resource environments, specifically with Sub Saharan African languages.

About the job
In this role, you will work alongside our teams of AI researchers, linguists, and data scientists to broaden representation and access of currently under-represented languages in Natural Language Processing (NLP) via transformative models, efficient data collections, and community engagement.

As a research-focused Software Engineer, you will set up large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

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 and data as open source, and apply research to Google products.


Responsibilities
Contribute to a NLP research agenda focused on NLP for languages currently under-represented in technology.
Work with a team of research scientists, engineers, linguists, and program managers to perform experiment design and drive data collections, develop improved evaluation methodologies, and contribute machine learning algorithm improvements.
Prepare high-quality research publications for peer-reviewed conferences and journals.
Build (benchmark) datasets, environments, and systems to demonstrate AI capabilities and inspire research.