Staff Machine Learning Engineer (l4)

Twilio Twilio · Enterprise · India · Remote · Engineering

Staff Machine Learning Engineer to scope, design, and deploy machine learning systems into production, partnering with Product & Engineering teams to execute the roadmap for Twilio’s AI/ML products and services. The role involves understanding customer needs, building scalable data products, and owning end-to-end execution of large-scale ML solutions.

What you'd actually do

  1. Build and maintain scalable machine learning solutions in production
  2. Train and validate both deep learning-based and statistical-based models considering use-case, complexity, performance, and robustness
  3. Demonstrate end-to-end understanding of applications and develop a deep understanding of the “why” behind our models & systems
  4. Partner with product managers, tech leads, and stakeholders to analyze business problems, clarify requirements and define the scope of the systems needed
  5. Work closely with data platform teams to build robust scalable batch and realtime data pipelines

Skills

Required

  • 7+ years of applied ML experience with proficiency in Python
  • Strong background in the foundations of Machine Learning and building blocks of modern Deep Learning
  • Track record of building, shipping and maintaining Machine Learning models in production
  • Track record of designing and architecting large scale experiments and analysis
  • Understanding of frameworks like PyTorch, TensorFlow, or Keras
  • Familiarity with ML Ops concepts related to testing and maintaining models in production
  • Demonstrated ability to ramp up, understand, and operate effectively in new application / business domains
  • Experience designing and coding in big-data components such as DynamoDB or similar
  • Experience working in an agile team environment
  • Experience of working on AWS

Nice to have

  • Experience with Large Language Models

What the JD emphasized

  • Track record of building, shipping and maintaining Machine Learning models in production in an ambiguous and fast paced environment.
  • Track record of designing and architecting large scale experiments and analysis to inform product roadmap.

Other signals

  • Deploying ML systems into production
  • Building data products at global scale
  • End-to-end execution of large scale ML solutions
  • Track record of building, shipping and maintaining ML models in production