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.
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