Machine Learning Engineer, Twilio

• B.S. in Computer Science, Data Engineering, Electrical Engineering, Mathematics, or related field—or equivalent practical experience
• 3–5 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 (Spark, Flink, or Kafka Streams)
• Strong analytical thinking, communication skills, and a demonstrated sense of ownership, curiosity, and continuous learning
• 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!

Desirable

• Join Twilio’s rapidly-growing AI & Data Platform team as an Machine Learning Engineer
• 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
• 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

• Applications for this role are intended to be accepted until May 20, 2026 but may change based on business needs