Twilio
Mid
Ship ML pipelines that extract meaning from voice and messaging data on Twilio's Conversation Intelligence team
Twilio is hiring a Machine Learning Engineer for its Conversation Intelligence team to build and deploy end-to-end AI/ML pipelines — from data ingestion through production inference — that extract meaning from voice and messaging data at scale. This interview focuses on applied ML engineering fundamentals, production deployment practices, and working with LLMs inside a larger software system.
走进这场面试
免费 · 一场按这个岗位校准的真语音模拟
这场面试考什么
- End-to-end ML pipeline building: ingestion, feature engineering, model dev, validation, deployment
- Python and ML framework proficiency (PyTorch, TensorFlow, or JAX) plus NLP libraries
- Production ML operations: model versioning, experiment tracking, cloud infra (AWS/GCP/Azure)
- Monitoring production inference services via metrics, logging, telemetry against SLOs
- On-call rotations, progressive rollouts, and mitigation of production ML issues
- Using LLMs/SLMs within a larger software system
常见提问方向
Walk through an ML service or feature you built end-to-end and deployed to production
How you've instrumented a model service to detect performance or operational degradation
A production incident or rollout you handled during on-call — what mitigation did you apply
How you've used an LLM or SLM inside a system, beyond just calling an API
A design decision you made independently vs. one you escalated to a senior engineer
Your experience with model versioning and experiment tracking on a real project
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