Machine Learning & Data Engineer - L2

Summary

Join Twilio as a Machine Learning & Data Engineer and design, build, and maintain data pipelines, training workflows, and real-time inference services for AI-driven communications. You will transform raw interaction data into scalable systems trusted by internal and external teams for machine learning solutions. This role involves building and optimizing data pipelines, implementing model-training workflows, deploying inference services, automating data quality checks, and collaborating with various teams. You will also contribute to design and code reviews, instrument systems with metrics, participate in on-call rotations, and improve CI/CD pipelines. The position requires 1-3 years of experience in software, data, or ML infrastructure engineering and proficiency in Python or Java and SQL. Twilio offers competitive pay, generous time off, parental and wellness leave, healthcare, and a retirement savings program.

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
• 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

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
• Participate in on-call rotations and continually improve CI/CD pipelines

Preferred Qualifications

• Experience with real-time feature stores and online/offline consistency challenges
• 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
• Familiarity with Twilio products, messaging APIs, or other communications platforms
• Advanced degree in Computer Science, Data Science, or a related discipline

Benefits

• Competitive pay
• Generous time off
• Ample parental and wellness leave
• Healthcare
• A retirement savings program
• Health care insurance
• 401(k) retirement account
• Paid sick time
• Paid personal time off
• Paid parental leave