Machine Learning Engineer

Role Overview

Join Twilio’s rapidly growing AI & Data Platform team as a Machine Learning Engineer (L3) for the Trust Intelligence Platform. You will design, build, and operate cloud-native data and ML infrastructure that turns raw events into real-time intelligence for Twilio product teams and customers.

Responsibilities

• Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads.
• Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tools.
• Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets.
• Monitor and improve data quality, model performance, latency, and cost.
• Partner with product, data science, and security teams to ship resilient, compliant services.
• Automate deployment using CI/CD, infrastructure-as-code, and container orchestration best practices.
• Produce documentation, dashboards, and runbooks; share knowledge via code reviews and brown-bag sessions.
• Take ownership of problems and drive them to completion in line with Twilio’s “We are Builders” values.

Required Qualifications

• B.S. in Computer Science/Data Engineering/Electrical Engineering/Mathematics (or related) or equivalent practical experience.
• 3–5 years building and operating data or ML systems in production.
• Proficient in Python and SQL; strong software engineering fundamentals (testing, version control, code reviews).
• Hands-on experience with ETL/ELT orchestration (e.g., Airflow, Dagster) and cloud data warehouses (Snowflake, BigQuery, or Redshift).
• Familiarity with ML lifecycle tooling (MLflow, SageMaker, Vertex AI, or similar).
• Working knowledge of Docker and Kubernetes and at least one major cloud (AWS, GCP, or Azure).
• Understanding of data modeling, distributed computing, and streaming frameworks (Spark, Flink, Kafka Streams).
• Strong analytical thinking, communication skills, and a sense of ownership/curiosity.

Desired Qualifications

• Experience with Twilio Segment, Kafka/Kinesis, or other high-throughput event buses.
• Exposure to infrastructure-as-code (Terraform, Pulumi) and GitHub-based CI/CD.
• Practical knowledge of generative AI workflows, foundation-model fine-tuning, or vector databases.
• Contributions to open-source data/ML projects or published technical content.
• Domain experience in communications, marketing automation, or customer engagement analytics.