Twilio's L2 Machine Learning & Data Engineer designs, builds, and maintains the data pipelines, training workflows, and real-time inference services that power AI-driven communications. You will convert raw interaction data into reliable, scalable systems that internal product teams and external builders trust to prototype, deploy, and measure machine-learning solutions at global scale.
Requirements
- 1–3 years of professional experience in software, data, or ML infrastructure engineering.
- Proficiency in Python or Java and in SQL for data manipulation and analysis.
- Hands-on experience building ETL/ELT pipelines with tools such as Apache Spark, Flink, or Airflow.
- Familiarity with training and deploying ML models using scikit-learn, TensorFlow, or PyTorch.
- Working knowledge of containerization (Docker) and at least one major cloud platform (AWS, GCP, or Azure).
- Comfort with Linux, Git, and CI/CD workflows; ability to write clean, tested, maintainable code.
- Experience with real-time feature stores and online/offline consistency challenges.
Responsibilities
- Build and optimize batch and streaming data pipelines processing billions of interaction events per day.
- Implement reusable model-training and evaluation workflows on Twilio’s internal ML platform.
- Deploy, monitor, and troubleshoot low-latency inference services in Kubernetes and serverless environments.
- Automate data-quality checks, feature logging, and lineage tracking to guarantee trustworthy datasets.
- Collaborate with product, data-science, and DevOps partners to translate business goals into technical roadmaps.
- Contribute to design reviews, code reviews, and documentation to elevate engineering standards.
- Instrument systems with metrics, alerts, and dashboards that uphold reliability objectives.
Other
- Clear verbal and written communication skills and a demonstrated sense of ownership.
- Bachelor’s degree in Computer Science or a related field, or equivalent practical experience.
- Exposure to MLOps practices such as model versioning, feature governance, and automated retraining.
- Knowledge of distributed-systems fundamentals and performance tuning.
- Contributions to open-source ML or data-infrastructure projects.
