I interviewed for an MLE role at HubSpot earlier this year. Wanted to share what the process looked like because HubSpot ML isn't well-documented and I had basically zero info going in.
First thing to understand: HubSpot ML is mostly applied ML for their CRM/marketing products. Think email send-time optimization, lead scoring, deal probability, content recommendation. Not research. Not GenAI moonshots (well, there are some AI features now but the MLE roles I interviewed for were more classical). Keep that framing when you prep.
Recruiter screen. They asked about my ML background but spent time on product intuition too. "What ML models do you know that could help a sales rep prioritize their pipeline?" came up early. Have an answer.
Technical phone screen (90 min). Split: coding half + ML design half. Coding: Python, LeetCode-medium, nothing exotic. A sliding window problem. They cared about clean code and talking through tradeoffs. ML design: "Design a model to predict which leads are likely to convert within 30 days." They want you to walk through data, features, target definition, model choice, evaluation metrics, deployment/monitoring. I went with a gradient boosting approach (XGBoost), talked about class imbalance, precision vs recall tradeoff for the sales use case. They pushed on the metric choice specifically.
Onsite (5 rounds, virtual). 2x coding (same LeetCode-medium flavor, one had a data manipulation / pandas component) ML system design: I designed a content recommendation system for HubSpot's content hub product. Lots of discussion on cold-start, feedback loops, offline vs online evaluation. ML breadth: rapid-fire conceptual questions. Bias-variance, regularization, gradient boosting internals, recommendation system gotchas, a/b testing validity. Not gotchas, just depth checks. Behavioral: cross-functional work, influencing without authority, handling a model that underperformed in production.
Total: 4.5 weeks. Offer was around $195k base for senior level, Boston. RSU package was decent, not FAANG-tier but reasonable for the COL.
The ML bar felt solid but not punishing. Know your fundamentals and be able to reason about the product context.