Principal Machine Learning & Data Engineer

Who we are

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, fair, and transparent, but automation never makes the final call. Every hiring decision is made by real Twilions, ensuring a human touch at every step.

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.

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.

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!

• 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/No
  
  SQL 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, Sage Maker, 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.

• Graduate degree focused on machine learning, distributed systems, or applied statistics.
• Contributions to open‑source ML or data…