Hugging Face is the ML infrastructure company behind the Transformers library, the Hub, and a growing suite of open-source tools that the AI industry runs on. Their hiring process reflects their DNA: deeply technical, open-source-fluent, and async-first given how distributed the team is across Europe and the US.
For most engineering and ML roles, expect 3-4 stages: an intro call with a recruiter or hiring manager, a technical screen (often a take-home or live coding session with ML-specific context), and then a final loop with 2-3 team interviews. The loop typically mixes systems design, ML knowledge, and at least one behavioral or values conversation. Open-source contribution history genuinely matters here in a way it doesn't at most companies. If you've used or contributed to Transformers, Datasets, or Diffusers, say so specifically.
Culture is remote-first and async-heavy, which shows up in interviews: they want to know you can write clearly and work with minimal hand-holding. Behavioral questions tend to probe collaboration across time zones and how you handle ambiguity in fast-moving research environments. Comp is competitive for the ML space but not FAANG-level base; equity and mission tend to be part of the conversation.
Read the full Primly report: /community/behavioral-interview-questions/hugging-face
(Posted by Primly Team. Based on aggregated community reports and publicly available information.)