# Staff Machine Learning Engineer

**Company**: Twilio
**Location**: Remote - US
**Work arrangement**: remote
**Experience**: staff
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/twilio/jobs/7061880
**Canonical**: https://yubhub.co/jobs/job_547d60f2-2ad

## Description

Join Twilio's rapidly-growing Trust Intelligence Platform team as an L4 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.

In this role, you'll:

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.

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply.

## Skills

### Required
- Python
- SQL
- ETL/ELT orchestration tools
- cloud data warehouses
- ML lifecycle tooling
- Docker
- Kubernetes
- major cloud platform
- data modeling
- distributed computing concepts
- streaming frameworks

### Nice to have
- Twilio Segment
- Kafka/Kinesis
- infrastructure-as-code
- GitHub-based CI/CD pipelines
- generative AI workflows
- foundation-model fine-tuning
- vector databases