Principal Machine Learning & Data Engineer

TwilioTwilio·Remote(Remote - US)
Data & Analytics

WFA Digital Insight

The demand for skilled machine learning engineers has grown significantly, with a 25% increase in job postings over the past year. As companies like Twilio continue to innovate and expand their offerings, the need for experts who can design and operate complex ML-and-data platforms has become increasingly important. With the rise of remote work, the ability to collaborate and communicate effectively with distributed teams is crucial. Twilio's commitment to remote-first work and strong culture of connection and global inclusion makes it an attractive option for those looking to make a meaningful impact. Before applying, candidates should be aware of the high level of technical expertise required for this role and the importance of staying up-to-date with the latest developments in the field.

Job Description

About the Role

The Principal Machine Learning & Data Engineer will play a critical role in leading the design, build, and operation of Twilio's internal ML-and-data platforms. This is a key position that will have a significant impact on the company's ability to deliver innovative solutions to its customers. The successful candidate will be responsible for architecting cloud-native pipelines, model-serving infrastructure, and developer tooling that allow Twilio's product teams to iterate rapidly and safely at scale.

As a member of the engineering team, you will work closely with cross-functional teams to break down complex initiatives into executable roadmaps. You will also mentor staff and senior engineers, raising the technical bar through code reviews and pair programming. Twilio is committed to creating a culture of experimentation, data-driven decision-making, and continuous improvement, and the Principal Machine Learning & Data Engineer will be a key player in driving this culture forward.

What You Will Do

  • 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.

What We Are Looking For

  • 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 cloud-native technologies, including AWS, GCP, or Azure.
  • Experience with containerization using Docker and Kubernetes.
  • Strong understanding of data structures, algorithms, and software design patterns.
  • Excellent communication and collaboration skills.

Nice to Have

  • Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Knowledge of agile development methodologies and version control systems such as Git.
  • Familiarity with CI/CD pipelines and automation tools such as Jenkins or CircleCI.

Benefits and Perks

  • Competitive salary and equity package.
  • Comprehensive health, dental, and vision insurance.
  • 401(k) matching program.
  • Flexible paid time off and remote work options.
  • Professional development opportunities and conference sponsorships.
  • Access to the latest tools and technologies.

How to Stand Out

  • Be prepared to discuss your experience with machine learning and data engineering, including specific projects and technologies used.
  • Show a strong understanding of cloud-native technologies and containerization.
  • Highlight your ability to collaborate and communicate effectively with cross-functional teams.
  • Be ready to provide examples of your experience with MLOps best practices and automated testing.
  • Emphasize your passion for staying up-to-date with the latest developments in the field and your commitment to continuous learning.
  • Use your portfolio or GitHub repository to demonstrate your technical skills and experience.

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