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Senior Research Scientist, Model Evaluation

Cohere · Toronto

RemoteSeniorScience and researchModelingModelingPosted 9 months ago
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About this role

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company co-headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris. Join us! Why this role? Evaluation is critical to making progress in scaling intelligence. As models continue to become superhuman in many real-world use cases, we must continue to develop new evaluation techniques that accurately reflect what models are already capable of, as well as set the agenda for what future models should be capable of. In this role, you are responsible for creating these next-generation evaluation methods and infrastructure to measure LLM progress. As a Senior Research Scientist, Model Evaluation, you will: - Create ambitious new evaluation benchmarks that push the limits of what our models can accomplish. - Work on highly cross-functional teams to translate model feedback into trustworthy, repeatable evaluations. - Conduct research to advance the state-of-the-art in LLM evaluation methods, including training LLM judges; refining LLM-based data synthesis pipelines; and improving evaluation efficiency. - Build scalable and reusable tools for digging into model performance. You may be a good fit if: - You enjoy rapidly building prototypes that demonstrate the boundaries of what LLMs are capable of, and you

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