just got out of mercury's ML engineering loop, rejecting an offer from them. writing this up because ML roles at fintechs are a different animal from big tech and i want to share what i learned.
first: mercury doesn't have a huge ML org. this is a small focused team building models that touch fraud detection, credit risk signals, and product recommendations inside the dashboard. you will not be working on LLMs for their own sake. if you want that, look elsewhere.
recruiter screen: 30 min, pretty thorough for a first call. she actually asked about my specific model experience (tabular data vs. NLP vs. cv) and was upfront that the team uses tabular models heavily. i have a mostly NLP background, which she flagged. that honesty was appreciated even if it signaled a rough road.
technical phone screen (60 min): two parts. first half was a ML fundamentals conversation: overfitting, regularization, how you'd handle class imbalance in a fraud dataset (spoiler: that's not an accident). second half was a coding problem in python, not an algorithm puzzle but more like writing feature engineering code given a dataframe. think pandas/polars style manipulation.
virtual onsite (4 rounds): ML system design. i was given a fraud detection scenario and asked to walk through the full pipeline: feature engineering, model selection, serving architecture, monitoring in production. they cared a lot about the monitoring piece specifically. model drift in fraud detection is a real operational problem and they want to know if you've thought about it. coding round (more feature engineering + some sklearn). product/stakeholder round (how do you explain model decisions to non-technical people, specific examples). behavioral.
what they're really looking for: practical ML for a financial context. if you can talk about class imbalance, data leakage in time-series splits, model explainability (shap values came up naturally in my conversation), and monitoring in prod, you're well positioned. research pedigree matters less than applied chops.
comp offer was around 200-220k TC. i passed because the team size was too small for where i am in my career right now, not because of anything negative about the process.