Staff, Machine Learning Engineer (l4)

Twilio Twilio · Enterprise · India · Remote · Engineering

Staff Machine Learning Engineer at Twilio to scope, design, and deploy ML systems into production. This role involves partnering with Product & Engineering teams to execute the roadmap for Twilio’s AI/ML products and services, understanding customer needs, building data products at global scale, and owning end-to-end execution of large-scale ML solutions. Requires a deep background in ML engineering and a track record of solving data & ML problems at scale.

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 in an ambiguous and fast paced environment.
  • Track record of designing and architecting large scale experiments and analysis to inform product roadmap.
  • Clear understanding of frameworks like - PyTorch, TensorFlow, or Keras, why and how these frameworks do what they do
  • Familiarity with ML Ops concepts related to testing and maintaining models in production such as testing, retraining, and monitoring.
  • Demonstrated ability to ramp up, understand, and operate effectively in new application / business domains.
  • Explored modern data storage, messaging, and processing tools (Kafka, Apache Spark, Hadoop, Presto, DynamoDB etc.) and demonstrated experience designing and coding in big-data components such as DynamoDB or similar
  • Experience working in an agile team environment with changing priorities
  • 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

  • Deploy ML systems into the real world
  • Own end-to-end execution of large scale ML solutions
  • Build and maintain scalable ML solutions in production
  • Train and validate models
  • Ship and maintain ML models