Product Manager Jobs at GitHub

GitHub PM interview guide

GitHub's PM loop runs about 4 to 6 weeks (longer, 6 to 10 weeks, for Copilot AI roles): a 30 minute recruiter screen, a hiring manager interview, and a final onsite of roughly 5 rounds, with the final round often built around a take-home or live presentation of an ambiguous prompt. The one thing that defines the loop is developer empathy: GitHub wants to see you reason from real developer and open-source maintainer workflows, not generic consumer product sense.

The interview process

1. Recruiter screen (30 min). Phone call on your background, motivation, and why GitHub specifically. They check basic fit and walk you through team dynamics and the loop ahead.
2. Hiring manager interview (about 60 min). Deeper dive on your product experience, how you set vision and roadmaps, and how you work with engineering and design. For Copilot and AI roles this round goes hard on AI and LLM depth and is sometimes led by an engineering manager.
3. Take-home or case assignment. Many loops include a case study you prepare in advance, often an ambiguous product prompt where you scope the problem, propose a solution, and define rollout and success metrics. You present it live in the final round.
4. Product sense and design round (60 min). Design a feature for GitHub's actual surfaces (maintainer tools, Actions, Copilot). Evaluated on customer empathy for developers, structured thinking, and how you measure success.
5. Analytical and execution round (60 min). Metrics definition, root-cause debugging of a degraded feature, and A/B or adoption thinking. They want to see you turn a vague product outcome into the right metrics and a clear investigation.
6. Strategy and estimation round (60 min). Market sizing for GitHub's enterprise business plus competitive positioning against GitLab, Bitbucket, and frontier AI models. Tests business judgment and trade-off reasoning.
7. Behavioral and leadership round (60 min). STAR-format stories on conflict, influence without authority, and hard roadmap trade-offs. They probe ownership and how you create clarity in ambiguity.
8. What they're really testing. Three signals carry the most weight: developer and open-source empathy (can you reason from real maintainer and engineer workflows), structured problem-solving under ambiguity (the open-ended presentation prompt is the core filter), and for AI roles, genuine depth on LLMs, evals, and where AI coding tools still fall short.

Likely PM interview questions

1. A new law passes in the United States requiring real legal names on internet properties instead of user handles. Prepare a 30 minute presentation: document your assumptions, scope the problem, develop a solution, and explain how you would roll it out and measure success.Product sense
2. GitHub wants to build a new feature to help open-source maintainers manage community engagement more effectively. Design it: user personas, key functionality, and how you would measure success.Product sense
3. How would you improve GitHub Actions to make it more appealing to enterprise customers who are hesitant about its complexity or security?Product sense