Machine Learning Engineer

Role overview

Join Twilio’s AI & Data Platform team as an L3 Machine Learning Engineer 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 powering customer interactions.

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 tooling.
• Ingest 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 and compliant services.
• Automate deployments via CI/CD, infrastructure-as-code, and container orchestration best practices.
• Create documentation, dashboards, and runbooks; share knowledge via code reviews and brown-bag sessions.
• Demonstrate Twilio’s builder mindset by taking ownership and driving problems to completion.

Required qualifications

• B.S. (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.
• Proficiency in Python and SQL; comfortable with software engineering fundamentals (testing, version control, code reviews).
• Hands-on ETL/ELT orchestration (e.g., Airflow, Dagster) and cloud data warehouses (Snowflake, BigQuery, or Redshift).
• Familiarity with ML lifecycle tooling (e.g., MLflow, SageMaker, Vertex AI).
• 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, or Kafka Streams).
• Strong analytical thinking, communication skills, and a sense of ownership/continuous learning.

Desired / nice-to-haves

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