Finished a Twilio MLE loop in Q1 2026. Sharing because the ML interview landscape varies so much company to company and I had basically no signal going in.
Twilio isn't a model-building company in the same sense as Google Brain or a pure-play AI lab. Their ML work is largely applied: fraud detection, spam filtering on SMS/voice, call quality prediction, intelligent routing. That framing matters a lot for how they interview.
Round 1: ML fundamentals. Talked through classification models, precision vs recall tradeoffs (very relevant for spam filtering use cases), handling class imbalance, and feature engineering. Pretty solid foundational coverage. No take-home, no Kaggle-style problem.
Round 2: ML system design. This was the most interesting round. The prompt: design an ML system to detect fraudulent SMS messages in near-real-time. We had to cover data collection, feature pipeline, model choice, latency constraints, monitoring for model drift, and how you'd retrain. This is where candidates get separated. You can't give a textbook answer here. The latency and scale constraints (Twilio moves a lot of messages) make it a real engineering problem.
Round 3: Coding (Python). One algorithmic problem (arrays/sliding window, medium difficulty), and one problem that was data-manipulation heavy: given a dataset of message delivery events, compute certain aggregations. More applied data work than pure LC.
Round 4: Behavioral. Two rounds actually, one with the hiring manager. They wanted to know about times I drove an ML project end-to-end, how I handled disagreements with product about model deployment decisions, and how I communicated model performance to non-technical stakeholders.
No deep learning required for this role. I barely talked about neural nets. It was more classical ML and pragmatic engineering. If you're coming from a pure research background this might feel different.
Offer was in the $195k-$215k base range for senior MLE, Bay Area. Solid but not hyperscaler numbers.