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.
A round-by-round breakdown of the Hugging Face loop is being compiled from candidate reports. In the meantime, the role pages below show the questions, process pattern, and salary signal for each function, and the general structure tends to be a recruiter screen, a role-specific technical assessment, an onsite loop, and a final calibration or decision step.
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 sourced from BLS, ONS, and Levels.fyi reference data. 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 4.2An external resource we recommend. Educative is not affiliated with us and we earn nothing from this link.