Twilio
Senior
Staff Machine Learning Engineer (L4) at Twilio — ship and scale production ML systems for AI/ML products
This is a staff-level ML engineering role focused on scoping, designing, and deploying machine learning systems into production at global scale, partnering closely with Product and Engineering to execute Twilio's AI/ML roadmap. You'll train and validate both deep-learning and statistical models, build robust batch and realtime data pipelines, and drive engineering standards through mentoring and code review. The role requires 7+ years of applied ML experience, deep familiarity with PyTorch/TensorFlow/Keras internals, MLOps practices, and comfort with big-data tooling like Kafka, Spark, or DynamoDB.
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What this interview tests
- End-to-end ML system design and productionization
- Deep learning vs. statistical modeling tradeoffs
- MLOps: testing, retraining, monitoring models in production
- Big-data pipeline design (Kafka, Spark, DynamoDB)
- Scoping ambiguous problems with product/business stakeholders
- Technical mentorship and engineering standards
Common question themes
Walk through an ML model you took from design to production
Deep learning vs statistical model choice for a given use case
Internals of PyTorch/TensorFlow/Keras and how that informed a decision
Designing a scalable batch or realtime data pipeline
Defining scope for an ambiguous ML problem with product stakeholders
Driving ML Ops practices (testing, retraining, monitoring) across a team
Mentoring engineers and raising code review / testing standards
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