Twilio

Machine Learning & Data Engineer

Twilio

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Salary

💰 $184,500 - $271,300 per year

Job Level

SeniorLead

Tech Stack

AWSCloudDistributed SystemsGoJavaKafkaKubernetesNoSQLPythonScalaSparkSQLTerraform

About the role

  • Twilio is remote-first and offers remote work; the role is remote (not eligible in CA, CT, NJ, NY, PA, WA).\n
  • This role is remote and part of Engineering; L5 Machine Learning & Data Engineer to lead the design, build, and operation of the internal ML-and-data platform that powers every customer interaction. You will architect cloud-native pipelines, model-serving infrastructure, and developer tooling that allow Twilio’s product teams to iterate rapidly and safely at scale, advancing our mission to unlock the imagination of builders.\n
  • Responsibilities: Architect and evolve Twilio’s end-to-end ML and real-time data platforms for reliability, security, and cost efficiency.\n
  • Design scalable feature stores, streaming and batch pipelines, and low-latency model-serving layers on AWS.\n
  • Implement MLOps best practices—automated testing, CI/CD, monitoring, and rollback—for hundreds of daily deployments.\n
  • Own system design reviews, threat modeling, and performance tuning for high-volume communications workloads.\n
  • Lead cross-functional engineering efforts, breaking down complex initiatives into executable roadmaps.\n
  • Mentor staff and senior engineers, raising the technical bar through code reviews and pair programming.\n
  • Partner with Product, Security, and Compliance to meet stringent privacy and governance requirements (HIPAA, SOC 2, GDPR).\n
  • 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.