Company profile
Hugging Face hosts the largest open-source model and dataset hub and maintains the Transformers, Datasets, and Diffusers libraries that the open-ML community is built on. Interviews are remote-first, fast, and biased toward candidates with public open-source track records, often with a take-home that maps directly to a real platform problem.
Hugging Face operates as a research organization, so interviews at this kind of employer usually go deep rather than broad: past projects, publications where relevant, and how you reason about open problems. Panels tend to include working researchers rather than dedicated interviewers. The role pages below show what to prepare for ml engineer, ai engineer, ai infrastructure engineer positions.
Hugging Face hires across several engineering and product functions, and the loop shifts with each one. Open a role for the reported questions, the round-by-round focus, and a salary band for that function.
ML engineer interview questions and process at Hugging Face.
AI engineer interview questions and process at Hugging Face.
AI infrastructure engineer interview questions and process at Hugging Face.
AI red team engineer interview questions and process at Hugging Face.
AI research engineer interview questions and process at Hugging Face.
MLOps engineer interview questions and process at Hugging Face.
Backend engineer interview questions and process at Hugging Face.
Analytics engineer interview questions and process at Hugging Face.
Approximate senior median pay for Hugging Face's core roles, anchored to New York and modelled from BLS, ONS, and Levels.fyi reference medians. These are market bands for the role and city, not Hugging Face offers. Open a role for the full city-by-city table.
Hugging Face holds a 4.2 Glassdoor rating. External review scores are directional signals. Treat them as context alongside the specific team, location, level, and hiring manager you are interviewing with.
Glassdoor 4.2