Original listing text, shown exactly as published by the company.
Responsibilities
In this role, you’ll
- 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.
Qualifications
Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table!
*Required
- 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.
