went through the airbnb MLE loop about five weeks ago. sharing raw notes.
airbnb is heavily recsys/ranking. if you're coming from pure NLP or CV, you're not disqualified but you need to speak the ranking language. think: how do you balance exploration vs exploitation in a recommendation setting, offline vs online evaluation metrics, feature pipelines for user-item interaction data.
My loop structure:
Coding (2 rounds): one standard coding round, one ML coding round. the standard round was a medium-difficulty graph problem. the ML coding round was more interesting: implement a simplified version of a nearest-neighbor search over embeddings, evaluate a few distance functions, and then discuss how you'd scale this. no sklearn, write the math yourself. they gave me numpy.
ML system design (1 round): i got 'design a ranking system for airbnb search results'. this is as on-brand as it gets. i started with clarifying questions: what's the primary metric (bookings? clicks? satisfaction scores?), what's the latency constraint, what signals do we have. they wanted to see the full pipeline: candidate retrieval, feature engineering, model choice (they asked me to compare gradient boosted trees vs a two-tower neural network for retrieval), offline eval with holdout sets, online A/B test design, and monitoring for model drift. we spent 20 minutes just on the monitoring piece. they care a lot about how you'd detect that your model is degrading in production.
ML depth / research interview: this felt more like a PhD-style discussion. they gave me a paper-adjacent problem about handling position bias in ranking data (users click on top results more regardless of quality). we talked about inverse propensity scoring, unbiased learning-to-rank methods, and logged bandit feedback. if you don't know what position bias is, add it to your prep list.
Behavioral: same format as every other role. "for 'be a host'..." etc.
comp note: the L5 MLE offer i received was base around $200-220k SF range plus RSUs. i framed it as ballpark "a friend got."
prep resources that actually helped: the Airbnb engineering blog (they've posted about their ranking and search systems), the RecSys conference papers on learning-to-rank, and honestly just being able to talk concretely about tradeoffs rather than naming-dropping buzzwords.