Twilio

Machine Learning & Data Engineer - P5

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

  • 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.