Twilio
Mid
Build the data and ML infrastructure powering real-time intelligence across Twilio's products
Twilio's AI & Data Platform team needs a hands-on Machine Learning Engineer to architect data pipelines, feature stores, and ML training/inference workflows that turn raw events from Messaging, Voice, and Segment into real-time intelligence. This interview focuses on production ML systems engineering: pipeline architecture, MLOps tooling, and operational rigor, not pure modeling theory.
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What this interview tests
- Data pipeline and feature store architecture (batch + real-time)
- ML training, evaluation, and inference workflow reproducibility (MLOps)
- Event stream integration (Kafka/Kinesis, Segment-style event buses)
- Data quality, model performance, latency, and cost monitoring
- CI/CD, infrastructure-as-code, and container orchestration for ML services
- Cross-functional collaboration with product, data science, and security
Common question themes
Design a feature store that serves both batch and real-time ML workloads
Walk through integrating a high-throughput event stream into an analytics-ready dataset
Describe a production ML pipeline failure — how did you detect, triage, and fix it
How do you make an ML training/evaluation workflow reproducible across teams
Explain your approach to CI/CD and infra-as-code for deploying ML services safely
Tell me about a time you had to balance model latency against cost or accuracy in production
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