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twilio
At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences.
Our dedication to remote-first work , and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant team with diverse experiences making a global impact each day. As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences that make work feel truly rewarding. Your career at Twilio is in your hands.
We use Artificial Intelligence (AI) to help make our hiring process efficient. That said, every hiring decision is made by real Twilions! . See yourself at Twilio Join the team as Twilio’s next 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.
Twilio’s next 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.
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. Desired: 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.
Location This role will be remote, but is not eligible to be hired in CA, CT, NJ, NY, PA, WA. Travel We prioritize connection and opportunities to build relationships with our customers and each other. For this role, you may be required to travel occasionally to participate in project or team in-person meetings.
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