Machine Learning & Data Engineer

Summary

Join Twilio as an L5 Machine Learning & Data Engineer to lead the design, build, and operation of the internal ML-and-data platform. You will architect cloud-native pipelines, model-serving infrastructure, and developer tooling to enable Twilio's product teams to iterate rapidly and safely at scale. This role involves architecting and evolving Twilio’s end-to-end ML and real-time data platforms, designing scalable feature stores and pipelines, implementing MLOps best practices, and leading cross-functional engineering efforts. You will also mentor staff and senior engineers, partner with other teams to meet stringent requirements, and champion a culture of experimentation and continuous improvement. The position requires a Bachelor’s or higher degree in a relevant field, 7+ years of experience building and operating production data or machine-learning systems, and expertise in various technologies and methodologies. The role is remote but has occasional travel requirements and is not eligible for hiring in certain states.

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

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

Preferred Qualifications

• 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

Benefits

• Health care insurance
• 401(k) retirement account
• Paid sick time
• Paid personal time off
• Paid parental leave