Design a recommendation system for Jira issues

Q: Design a recommendation system for Jira issues

This is a ML System Design interview question from Atlassian for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

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Scenario

Design a recommendation system for Jira that helps users work more efficiently.

Assume Jira contains:

Issues/tickets (type, priority, status, components, labels, assignee, watchers, comments, description)
Users (team/org, role, skills, historical activity)
Projects/boards (workflows, components)

Product requirements (choose and justify)

Design recommendations for one or more of the following:

Assignee recommendation : suggest the best assignee for a new issue.
Similar/duplicate issue recommendation : show similar tickets to reduce duplicates.
Next-best issue : recommend what a user should work on next.
Watcher/mention suggestions : suggest relevant stakeholders.

Constraints & expectations

Near real-time suggestions when creating or viewing an issue (e.g., p95 < 200 ms).
Handle cold-start for new projects/users.
Must support permissions: users should never see tickets they can’t access.
Provide an MVP plan and a path to iteration.

Deliverables

Explain:

Goals and success metrics
Data and labeling strategy
Model approach (candidate generation + ranking, or other)
Online serving architecture and storage
Offline training pipeline and evaluation
Monitoring, feedback loops, and common failure modes

Design a recommendation system for Jira issues

Scenario

Product requirements (choose and justify)

Constraints & expectations

Deliverables

Solution

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