Data Annotation Specialist, Safety

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. Please Note: This is a part-time, remote, independent contractor position available within Canada or the United States. We seek candidates who are able to commit to 16 hours per week minimum at a 45 CAD/hour or 40 USD/hour contract rate, depending on your location, consisting of 30/hour base pay plus 15 CAD or 10 USD/hour hazard pay. This role is BYOD 💻 - Bring Your Own Device (laptop). 12 months contract with potential for extension. As a Data Annotation Specialist on safety task, you will: - Evaluate and improve model safety: Label, rank, audit, and refine human- and model-generated text to improve safety, quality, and policy alignment, including content that may be sexual, violent, or psychologically disturbing. - Apply nuanced safety judgment: Assess model outputs against detailed safety guidelines, rubrics, and style standards, making consistent decisions across ambiguous, sensitive, and context-dependent cases. - Create prompts and safety test cases: Write realistic prompts, user scenarios, and adversarial examples that help evaluate model behavior across safety categories and uncover unsafe, evasive, over-refusing, or policy-inconsistent responses. - Support quality and calibration: Identify annotation inconsistencies or unclear guidelines, and provide actionable feedback on recurring edge cases, model failures, and opportunities to improve data quality. - Work with precision and independence: Complete annotation tasks with strong attention to detail, while being comfortable working independently in a globally distributed, asynchronous team environment. You may be a good fit if you have: - 1+ years of experience in Content Moderation,Trust and Safety, AI data annotation, LLM evaluation, or a related analytical role, with exposure to quality assurance, red teaming, and/or prompt engineering preferred. - Experience applying detailed guidelines to complex and sensitive content, with strong contextual and sociocultural judgment and the ability to recognize and manage personal bias. - Emotional resilience: Comfort working with content that contains unsafe, explicit, and/or toxic content, including content of a sexual, violent, or psychologically disturbing nature. - Excellent command of written English and the ability to clearly justify content evaluations, including why an output is safe, unsafe, high-quality or low-quality. Bonus points if you are fluent in another language! - Strong attention to detail and commitment to accuracy, with the ability to maintain consistency across high-volume and monotonous tas