GitHub
Home of Copilot: AI pair programmer, code review and coding agent
Interview in a few hours? A quick sprint for GitHub
AI in interviews
No public policy found. Assume no AI assistance in live rounds unless your recruiter says otherwise.
Typical interview loop
How GitHub usually runs it for applied AI and LLM engineer roles.
- Focus: Background, remote fit
Recruiter screen
A 30-minute call on your background, motivation and comfort with GitHub's remote-first way of working.
- Focus: Code quality, tests, Git
Take-home exercise
A time-boxed, work-like coding task done in your own editor in a private repo and submitted as a pull request, then scored anonymously against a rubric with automated tests.
- Focus: Experience fit, team context
Hiring manager conversation
A behavioural and experience-fit chat with the hiring manager about the team and what you'd work on.
- Focus: Collaboration, review judgment
Pairing and code review
Collaborative sessions that may extend your take-home, pair on new code with engineers, or have you review a pull request and give specific, kind feedback.
- Focus: Scale, API trade-offs
System design
A design discussion on developer-platform infrastructure, weighing API design, scale and consistency against latency.
- Focus: Remote collaboration
Behavioral
A session on teamwork, conflict and working well asynchronously across a distributed team.
How it weights your plan
Selecting GitHub when you build a plan multiplies each topic’s priority score:
Official prep and resources
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