Cohere
Cohere Data Annotation Specialist Interview
Focus areas and question themes aggregated from 2 current openings — pick any opening below and practice a voice mock calibrated to it.
Cohere Data Annotation Specialist mock interview
A live voice mock calibrated to this role — real questions, the real follow-up rhythm, and a score at the end. Free to start.
This is contract annotation work, not product engineering: both postings hire part-time independent contractors to review, debug, and grade AI coding-agent output that feeds back into Cohere's model training and evaluation. One track leans on data science tasks in Python and SQL, the other on general software engineering across several languages.
What this interview tests
- Hands-on coding fluency — Python with numpy, pandas, and SQL for the data science track; Python, Java, JavaScript, Go, and SQL for the broader software engineering track.
- Debugging and code review judgment — Finding a subtle bug that looks correct on the surface, and verifying the correctness of a SQL query or generated script.
- Evaluating AI agent output — Judging whether a coding agent's trajectory actually solved the task, not just whether the output looks plausible.
- Written communication — Both postings screen on a writing sample and expect clear, structured, proofread feedback, since the job output is written evaluation.
- API and architecture literacy — The software engineering track specifically tests critiquing API designs and navigating unfamiliar repository architecture quickly.
- Familiarity with coding agents specifically — Both postings ask where tools like Claude Code, Cursor, Codex, or OpenCode tend to fail, not just whether you've used them.
Common question themes
Walk me through a data science task you solved end-to-end in Python.
Named question theme for the Data Science annotation track.
Review this code snippet or diff and identify what's wrong with it.
Named question theme for the Software Engineering annotation track.
How do you verify the correctness of a SQL query or generated script?
Named question theme for the Data Science annotation track.
How would you evaluate whether an AI coding agent's output actually solved the task?
Core judgment call named on both postings.
Critique an API design — what's missing or wrong given the stated requirements?
Named question theme for the Software Engineering annotation track.
Describe a time you caught a subtle bug that looked correct on the surface.
Named question theme for the Software Engineering annotation track.
What have you noticed about where coding agents like Claude Code or Codex tend to go wrong?
Named question theme on both postings, testing real hands-on agent experience.
Likely format
Both postings state the same loop: an initial resume and writing-sample screening, then a virtual annotation test that pairs a take-home (data science or coding, depending on track) with a writing sample, then a video screen with the Operations team. The software engineering posting adds an explicit offer stage after that screen.
All 2 Cohere openings in this role
Frequently asked questions
Is this a full-time engineering job at Cohere?
No. Both postings are for part-time independent contractors — a minimum of 16 hours per week on a 12-month contract — doing evaluation and annotation work, not product engineering.
Do I need machine learning experience to qualify?
Neither posting asks for ML research experience. The bar is hands-on coding and data science fluency plus the judgment to evaluate whether an AI agent's output is actually correct.
What does the take-home test look like?
Per the stated process, it's a virtual annotation test: a take-home exercise in your track (data science or coding) submitted alongside a writing sample, evaluated before you reach a video screen with Operations.