About this role
Who are we?
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!
Why this role?
We are on a mission to build machines that understand the world and make them safely accessible to all. Data quality is foundational to this process. Machines (or Large Language Models to be exact) learn in similar ways to humans: by way of feedback. By labeling, ranking, auditing, prompting, red teaming, and correcting output, you will improve Large Language Model’s performance for iterations to come, thus having a lasting impact on Cohere’s tech.
Cohere is looking for Data Annotation Specialists with backgrounds and skills in Trust & Safety, Content Moderation, AI model evaluation, or related fields. This role is best suited for candidates who bring strong contextual judgment, cultural and bias sensitivity, and experience applying nuanced guidelines to complex or ambiguous content. Successful candidates will be highly detail-oriented, comfortable evaluating safety risks across different user intents and scenarios, and able to make consistent, well-reasoned decisions with a high degree of independence after onboarding.
IMPORTANT CONTEXT ON THIS ROLE: In this position, you will be asked to engage with human-generated and model-generated tasks which will sometimes mean intentional exposure to explicit content. Your annotations on these explicit tasks will be used to prevent the Large Language Model from generating unintentional, adversarial, toxic, or unsafe outputs. The types of explicit content you may be exposed to may include but are not limited to those of a sexual, violent, or psychologically disturbing nature.