Staff Machine Learning Engineer

See yourself at Twilio

Join the team as Twilio’s next L4, Machine Learning Engineer, Trust Intelligence Platform

About the job

Join Twilio’s rapidly-growing Trust Intelligence Platform team as an L4 Machine Learning. You will design, build, and operate the cloud-native data and ML infrastructure that powers every customer interaction, enabling Twilio’s product teams and customers to move from raw events to real-time intelligence. This is a hands-on, builder-focused role that offers clear technical ownership, mentoring, and growth inside a company defining the future of communications with AI.

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.
• Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets.
• Monitor, test, and improve data quality, model performance, latency, and cost.
• Partner with product, data science, and security teams to ship resilient, compliant services.
• Automate deployment with CI/CD, infrastructure-as-code, and container orchestration best practices.
• Produce clear documentation, dashboards, and runbooks; share knowledge through code reviews and brown-bag sessions.
• Embrace Twilio’s “We are Builders” values by taking ownership of problems and driving them to completion.

Qualifications

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table!

• B.S. in Computer Science, Data Engineering, Electrical Engineering, Mathematics, or related field—or equivalent practical experience.
• 4-8 years building and operating data or ML systems in production.
• Proficient in Python and SQL; comfortable with software engineering fundamentals (testing, version control, code reviews).
• Hands-on experience with ETL/ELT orchestration tools (e.g., Airflow, Dagster) and cloud data warehouses (Snowflake, BigQuery, or Redshift).
• Familiarity with ML lifecycle tooling such as MLflow, SageMaker, Vertex AI, or similar.
• Working knowledge of Docker and Kubernetes and at least one major cloud platform (AWS, GCP, or Azure).
• Understanding of data modeling, distributed computing concepts, and streaming frameworks.