did the GS ML engineer loop for a role in their risk and analytics group. this is not a research-flavored ML interview. just want to set that expectation upfront.
the loop: phone screen, take-home, three technical rounds, one behavioral.
phone screen: basics. ML fundamentals, a few probability questions, brief coding problem. filter round, nothing fancy.
take-home: build a classifier on a financial dataset, evaluate it, write up your methodology. they care about how you handle imbalanced classes (the data was very imbalanced) and how you interpret results for a non-technical audience. i wrote a 2-page PDF alongside my notebook, that seemed to land well.
technical round 1, ML depth: feature engineering for tabular/time-series data, regularization tradeoffs, ensemble methods. they asked how i'd approach overfitting in a model where i can't get more data. also: model monitoring in production. how do i detect that a model has gone stale. this is serious at GS because stale models in a risk context are a real liability.
technical round 2, coding: Python, data manipulation and some algorithmic content. one problem was clearly from the actual work they do (anonymized). medium difficulty. they let me ask clarifying questions.
technical round 3, system design: design an ML pipeline for real-time credit risk scoring. latency constraints were tight. they pushed on retraining cadence, feature store, rollback strategy.
behavioral: heavier than i expected. the theme was "how do you handle a situation where your model is right but stakeholders don't trust it." came up twice in different forms.
the GS ML role is closer to ML engineering in the production + reliability sense than to research or modeling innovation. if that's what you want, great fit. if you want to push the frontier on architectures, probably not the right place.