Research Internship

As a Cohere Research Intern, you will:

• Conduct cutting-edge machine learning research, building and training large language models. 
• Focus on research projects aimed at expanding the frontier of knowledge in language modelling and associate areas such as evaluation, multimodal models, optimisation etc.
• Disseminate your research results through the production of publications, datasets, and code.
• Contribute to research initiatives that have practical applications in Cohere’s product development. 

You may be a good fit if you:

• Are currently pursuing, or in the process of obtaining, a PhD in Machine Learning, NLP, Artificial Intelligence, or a related discipline. We will also consider exceptional non-PhD candidates.
• Are eligible for work authorization in the country of employment at the time of hire and maintain ongoing work authorization throughout the internship period. 
• Have experience using large-scale distributed training strategies, data annotation and evaluation pipelines, or implementing state of the art ML models.
• Are familiar with autoregressive sequence models, such as Transformers.
• Have strong communication and problem-solving skills with the ability to convey complex research findings clearly and succinctly. 
• Have knowledge, or are knowledgeable, of programming languages such as Python, C, C++, Lua, or related languages.
• Have knowledge of related ML frameworks such as JAX, Pytorch and Tensorflow.
• Have previous experience in building systems based on machine learning and deep learning techniques. 
• Demonstrate passion for applied NLP models and products.

Preferred Qualifications:

• Demonstrated expertise through publications in top tier venues in fields such as machine learning, NLP, artificial intelligence, computer vision, optimization, computer science, statistics, applied mathematics, or data science. 
• Proven ability to tackle analytical problems using quantitative methodologies. 
• Proficiency in handling and analysing complex, high-dimensional data from various sources.
• Experience in applying theoretical and empirical research to real-world problem-solving.