AI interview preparation from an archived Hugging Face recruiter Q&A (2022)

The archived recruiter Q&A dated 2022-05-17 answers this directly: Hugging Face's process used no LeetCode problems. Instead, candidates typically had two or three interviews with different team members, then an assessment tied to the specific project — sometimes a job talk, sometimes a quick coding exercise. If you are collecting machine learning interview questions to rehearse, this source points away from algorithm puzzles and toward evidence of independent work, open-source contribution, and a written statement of the impact you would create.

Interview stages: two or three conversations, then a role-specific take-home

The recruiter was explicit on the format: "We do not do leet code problems BUT we do give take home assessments, and they are different for each role/team." The order described was interviews first — two or three of them, each with a different team member — followed by an assessment whose content "changes based on the project you are interested in helping with." Assessment criteria also shift with "your experience and what active projects we have at the time," so the same title at the same company may not mean the same exercise. The source gives no named stages or fixed durations, and it claims the team prides itself on hiring faster than big tech. Location is not a filter: the recruiter answered "Nope!" to whether applicants must be in the US or Europe, stating they hire anywhere in the world and that having people in many time zones is what makes flexible daily hours possible.

What gets assessed: autonomy, open-source work, and a stated impact

The stated ideal candidate "is autonomous and a self-starter, already has experience contributing to open source projects, and wants to build amazing and responsible AI." Hard skills were described as a must-have because people work fairly independently and need to start and complete projects unaided; "having a strong opinion is a great place to start." Contributions do not have to be to Hugging Face or Gradio — other projects count, and the advantage of HF/Gradio work is mainly familiarity for the interview and the first weeks. Visibility matters: re-implementing existing things tends to be seen less than contributing to existing tools or creating tools that solve people's needs, and the recruiter noted many popular repositories carry good-first-issue issues. On presentation format there was "no strong preference," with the hunch that whatever you choose should be backed by GitHub code, since that is likely the work itself.

Application mistakes that get candidates skipped

The most expensive error was silence about impact: applications without "any write up as to how you can help us democratize machine learning" would "more than often" be skipped, and the recruiter called not explaining why you are awesome "a huge mistake." Three sentences about how you specifically would help was judged enough to go a long way. Other named mistakes were thin applications — little information, no stated interest, no GitHub link, no resume — which is why the advice is to be "very concise but informative" given that they receive hundreds of applications per role. Rejections are recoverable: applying again six months later was explicitly allowed. Internship hiring was described as opportunistic with no fixed minimum or maximum, limited mainly by wanting to avoid people leaving, and conversion to a full-time role was said to be the hope behind every internship.
Treat this as a 2022 snapshot of one company's approach, not evidence that any vacancy is open now or that these steps are unchanged; the source itself says assessments differ by role and team, so confirm the current process with the hiring team before preparing.