Principal Machine Learning & Data Engineer

Twilio is shaping the future of communications by delivering innovative solutions to businesses and empowering developers worldwide. The Principal Machine Learning & Data Engineer will lead the design, build, and operation of the internal ML-and-data platform, enabling rapid and safe iterations for Twilio's product teams.

Responsibilities:

• Architect and evolve Twilio’s end-to-end ML and real-time data platforms for reliability, security, and cost efficiency
• Design scalable feature stores, streaming and batch pipelines, and low-latency model-serving layers on AWS
• Implement MLOps best practices—automated testing, CI/CD, monitoring, and rollback—for hundreds of daily deployments
• Own system design reviews, threat modeling, and performance tuning for high-volume communications workloads
• Lead cross-functional engineering efforts, breaking down complex initiatives into executable roadmaps
• Mentor staff and senior engineers, raising the technical bar through code reviews and pair programming
• Partner with Product, Security, and Compliance to meet stringent privacy and governance requirements (HIPAA, SOC 2, GDPR)
• Champion a culture of experimentation, data-driven decision-making, and continuous improvement

Requirements:

• Bachelor's or higher in Computer Science, Engineering, Mathematics, or equivalent practical experience
• 7+ years building and operating production data or machine-learning systems at scale
• Expert fluency in Python and one compiled language (Java, Scala, Go, or C++)
• Hands-on mastery of distributed data frameworks (Spark/Flink), SQL/NoSQL stores, and streaming platforms (Kafka/Kinesis)
• Demonstrated success designing cloud-native architectures on AWS, including Terraform-managed infrastructure
• Deep knowledge of container orchestration (Kubernetes/EKS), service-mesh networking, and autoscaling strategies
• Practical experience implementing MLOps tooling such as MLflow, Kubeflow, SageMaker, or Vertex AI
• Strong grasp of model-lifecycle concerns—feature engineering, offline/online parity, A/B testing, drift detection, and retraining
• Proven ability to lead technical projects end-to-end and influence without authority across multiple teams
• Exceptional written and verbal communication skills, with a bias toward clarity and action
• Graduate degree focused on machine learning, distributed systems, or applied statistics
• Contributions to open-source ML or data infrastructure projects
• Experience with privacy-enhancing technologies (differential privacy, homomorphic encryption) or on-device inference
• Background in conversational AI, real-time communications, or large-language-model deployment at scale
• Exposure to compliance-heavy environments (HIPAA, PCI-DSS) and secure multi-tenant design patterns
• Published research, patents, or conference talks in ML systems or data engineering