Senior Research Engineer, Model Evaluation

About the Role

Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI. 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. We like to work hard and move fast to do what’s best for our customers. Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products. Join us on our mission and shape the future! 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 techniques to accurately measure our models' performance on frontier capabilities. In this role, you are responsible for creating next-generation evaluation methods and scalable infrastructure to measure LLM progress.

Responsibilities

• Develop evaluation benchmarks, datasets, and environments for measuring the bleeding edge of model capabilities
• Conduct research to push the state-of-the-art in LLM evaluation methods, including training LLM judges; improving evaluation efficiency; and scalably building high-quality datasets
• Build scalable tools for investigating and understanding evaluation results that are used by all members of technical staff at Cohere, as well as leadership and our CEO
• Learn from and work with the best researchers and engineers in the field

Requirements

• You enjoy pushing the limits of what LLMs are capable of, and you have built high-quality evaluation resources to measure those capabilities (datasets, simulators, environments, etc.)
• You have a track record of developing new methods and/or data to evaluate LLMs, e.g. publications at top-tier conferences, popular benchmarks, etc.
• You have deep experience building with and around LLMs, and you have built tools for analyzing and understanding their performance
• You have strong software engineering skills