Data Annotation Specialist, Safety
Cohere is hiring a Data Annotation Specialist, Safety for a remote role open to applicants in Canada. It pays CAD 45/hr and Level rates it ; you can apply on Cohere.
Level's read on this job
- Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
- This is listed as a remote role.
- The posting mentions prompt engineering, RAG and AI evaluation among its tools and skills.
- Cohere has 119 other open roles on Level.
- Posted in the last few days.
AI in 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.
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 tasks.
Strong execution in a remote environment, including good time management, comfort using new tools, and the ability to work independently in a global, asynchronous team.
The Candidate Journey:
Initial Screening - Once you have submitted your application our Talent Team will review your resume and writing samples.
Virtual Annotation Test - This assignment will test your written skill through various language-based tasks, such as a a writing sample, interacting with a chat bot, and more.
Video Screen - If selected to move forward, you will have a short video call with a member of our Operations Team!
Offer - Independent Contractor Agreement
As an independent contractor, you maintain control over how you complete your work and may work with multiple clients simultaneously, although we ask you to declare if any of these are with a direct competitor of Cohere and maintain IP confidentiality of the Cohere project. Independent contractors are not eligible for health benefits or other benefits provided to employees. Compensation for services is provided to contractors by contractors invoicing for services provided pursuant to the terms of our agreement with the contractor.
It is important to understand that as an independent contractor, continuous work is not guaranteed. The client-contractor relationship is fundamentally project-based, meaning engagements may be temporary, periodic, or intermittent based on our organizational needs and project availability. As an independent contractor, you should anticipate fluctuations in workflow and therefore compensation for services when Cohere does not require as many hours of services in a week.
Prospective candidates, please be advised: this role involves working with human generated and model generated tasks that may involve exposure to not safe for work (NSFW) text content as part of data annotation tasks, including explicit, offensive, or other inappropriate material.
If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply!
We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.
How we rate this
Data Annotation Specialist, Safety at Cohere rates 72 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide that one model's output is better than another's for a given task?
- How do you keep labeling instructions consistent across a large annotation team?
- Describe a typical day in a role like this one: which parts run through AI directly?
Adapt your resume
- List these exact terms on your resume: Prompt engineering, RAG, AI Evaluation, and AI Data Labeling. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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