Research Scientist

Introduction
IBM Research Scientists are charting the future of Artificial Intelligence creating breakthroughs in quantum computing discovering how blockchain will reshape the enterprise and much more. Join a team that is dedicated to applying science to some of today’s most complex challenges whether it’s discovering a new way for doctors to help patients teaming with environmentalists to clean up our waterways or enabling retailers to personalize customer service.

Your Role and Responsibilities
Applicants with a Natural Language Processing (NLP) background either with a proven or strong desire to examine and advance (large-scale) language models and a focus on inclusion in their studies and enablement.
Ideal applicants are forward thinkers who are willing to take initiative in coming up with new ideas and mechanisms to assess and drive fairness in tech design development and use.
Ideal applicants are also effective collaborators and enjoy working side-by-side with people of all backgrounds and disciplines.

“Todas as vagas são elegíveis para pessoas com deficiência e reabilitados”

Required Technical and Professional Expertise
PhD degree in a technical discipline (Machine learning/AI Statistics Computer Science Engineering Mathematics);
Proficiency in machine learning/deep learning;
Can develop data visualization or data collection interfaces (e.g. JavaScript Python);
Have experience implementing ML algorithms (e.g. PyTorch);
Ability to work in a team environment as well as independently;
Ability to understand and address socio-technical questions and solutions;
Strong problem-solving skills;
Proven record of strong written and oral communication skills;
Publications in ML and AI conferences.
Fluent English

Preferred Technical and Professional Expertise
Familiarity with multilingual natural language processing and large-scale language models (foundation models);
Familiarity with fine tuning RLHF prompt tuning techniques;
Familiarity with important social science concepts pertaining to issues of tech inclusivity fairness and ethics;
Knowledge of multimodal machine learning.