"Tell me about a time you led a machine learning project end-to-end and had to staff it, coordinate across functions, and scale it beyond the initial launch. I’m especially interested in how you handled ambiguity, competing priorities, and people challenges along the way."
At Meta, ML engineers often need to drive work across research, product, data engineering, infrastructure, and partner teams without always having direct authority. This question tests whether you can take ownership of a high-impact initiative, define the plan when the path is unclear, assemble the right team, and keep execution moving across surfaces like Facebook Feed, Instagram, Reels, or Ads. It also reveals how you make trade-offs, mentor others, and respond when staffing or alignment breaks down.
A strong answer uses one concrete example with clear business or user stakes, explains how you scoped the work and staffed the team, and shows how you coordinated execution through launch and scale-up. The best responses are specific, data-driven, and honest about what changed, what was hard, and what you learned as a leader.