Staff Machine Learning Engineer

Twilio Twilio · Enterprise · United States · Remote · Engineering

Staff Machine Learning Engineer to design, build, and operate cloud-native data and ML infrastructure for Twilio's Trust Intelligence Platform. This role focuses on scalable data pipelines, feature stores, and ML training/evaluation/inference workflows, integrating event streams from various Twilio products to enable real-time intelligence. Requires strong software engineering fundamentals, cloud platform experience, and familiarity with ML lifecycle tooling.

What you'd actually do

  1. Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads.
  2. Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tooling.
  3. Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets.
  4. Monitor, test, and improve data quality, model performance, latency, and cost.
  5. Partner with product, data science, and security teams to ship resilient, compliant services.

Skills

Required

  • Python
  • SQL
  • software engineering fundamentals
  • testing
  • version control
  • code reviews
  • ETL/ELT orchestration tools
  • cloud data warehouses
  • ML lifecycle tooling
  • Docker
  • Kubernetes
  • AWS
  • GCP
  • Azure
  • data modeling
  • distributed computing concepts
  • streaming frameworks
  • analytical thinking
  • communication skills
  • ownership
  • curiosity
  • continuous learning

Nice to have

  • Twilio Segment
  • Kafka
  • Kinesis
  • high-throughput event buses
  • infrastructure-as-code
  • Terraform
  • Pulumi
  • GitHub-based CI/CD pipelines
  • generative AI workflows
  • foundation-model fine-tuning
  • vector databases
  • open-source data/ML projects
  • published technical presentations/blogs
  • communications domain experience
  • marketing automation domain experience
  • customer engagement analytics

What the JD emphasized

  • 4-8 years building and operating data or ML systems in production
  • Proficient in Python and SQL
  • Hands-on experience with ETL/ELT orchestration tools
  • cloud data warehouses
  • ML lifecycle tooling
  • Docker and Kubernetes
  • major cloud platform (AWS, GCP, or Azure)
  • data modeling
  • distributed computing concepts
  • streaming frameworks
  • strong analytical thinking
  • demonstrated sense of ownership, curiosity, and continuous learning

Other signals

  • design, build, and operate cloud-native data and ML infrastructure
  • powers every customer interaction
  • enabling Twilio’s product teams and customers to move from raw events to real-time intelligence
  • hands-on, builder-focused role that offers clear technical ownership, mentoring, and growth