Senior Manager, Machine Learning

Twilio Twilio · Enterprise · United States · Remote · Engineering

Senior Engineering Manager for Twilio's Trust Intelligence Platform, responsible for building and managing a team that develops advanced machine learning models and data pipelines for real-time risk prediction and decision-making in communications. The role involves strategic planning, partnering with product teams, ensuring operational excellence, and managing highly critical risk platform tools in the cloud.

What you'd actually do

  1. Manage and Mentor a team of talented Machine Learning and Data Engineers with various levels of experience and positively influencing their careers.
  2. Partner with Product Managers and Architects to distill customer needs into actionable technical requirements and long-term roadmaps.
  3. Ensure best engineering practices & oversee end-to-end execution of large-scale ML solutions with operational excellence.
  4. Work with data platform teams to build robust, scalable batch and real-time data pipelines.
  5. Work closely with the Fraud and Compliance Operations team and the Data Analytics team to identify and understand the ever-changing landscape of fraud vectors and then take action to keep up with new forms of fraud.

Skills

Required

  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
  • 10–14+ years of total experience in machine learning /data engineering.
  • 5+ years of experience leading and managing engineering teams.
  • Proven track record of shipping and maintaining ML models in a fast-paced, production environment.
  • Experience developing highly-available full stack applications and distributed systems
  • Stellar communication, organization and management skills with proven track record in an agile environment.
  • Ability to explain your technical and business decisions succinctly as well as in detail.
  • Expert proficiency in Python.
  • Deep experience with PyTorch, TensorFlow, or Keras.
  • Experience with Kafka, Apache Spark, Hadoop, Presto, and DynamoDB.
  • Significant experience with AWS (specifically SageMaker, EKS, or ECS).
  • Familiarity with tools like Datadog and Grafana.

Nice to have

  • Familiarity with Java or Scala
  • Experience working in fraud/compliance domain.
  • Knowledge of telecommunications.
  • Familiarity with popular LLMs and latest developments.

What the JD emphasized

  • shipping and maintaining ML models in a fast-paced, production environment
  • highly-available full stack applications and distributed systems
  • Expert proficiency in Python

Other signals

  • build advanced machine learning models
  • robust data pipelines
  • fast, accurate risk predictions and decisions at scale
  • highly scalable systems
  • AI/ML roadmap
  • shipping and maintaining ML models in a fast-paced, production environment