Machine Learning Engineer
WFA Digital Insight
The demand for skilled machine learning engineers has skyrocketed in recent years, with the global market expected to grow by 30% annually. As a remote machine learning engineer at Twilio, you'll be at the forefront of this trend, designing and building the infrastructure that powers every customer interaction. With Twilio's commitment to remote-first work, you'll have the flexibility to work from anywhere and be part of a vibrant team making a global impact. Before applying, candidates should be aware that this role requires strong technical ownership, mentoring, and growth, as well as experience with cloud data warehouses and ML lifecycle tooling.
Job Description
About the Role
As a machine learning engineer at Twilio, you will be part of the rapidly-growing AI & Data Platform team, responsible for designing, building, and operating the cloud-native data and ML infrastructure that powers every customer interaction. This role offers clear technical ownership, mentoring, and growth opportunities, and you will be working closely with product, data science, and security teams to ship resilient, compliant services.The role is remote, and you will have the flexibility to work from anywhere, as part of a company that is committed to remote-first work and values diverse experiences from all kinds of industries. If you are passionate about machine learning, data engineering, and building scalable systems, this is an exciting opportunity to join a company that is shaping the future of communications.
What You Will Do
- Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads
- Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tooling
- Integrate event streams from Twilio products into unified, analytics-ready datasets
- Monitor, test, and improve data quality, model performance, latency, and cost
- Partner with product, data science, and security teams to ship resilient, compliant services
- Automate deployment with CI/CD, infrastructure-as-code, and container orchestration best practices
- Produce clear documentation, dashboards, and runbooks; share knowledge through code reviews and brown-bag sessions
- Embrace Twilio's values by taking ownership of problems and driving them to completion
- Collaborate with cross-functional teams to identify and prioritize project requirements
- Develop and maintain technical roadmaps, architectures, and strategies
What We Are Looking For
- B.S. in Computer Science, Data Engineering, Electrical Engineering, Mathematics, or related field—or equivalent practical experience
- 3-5 years building and operating data or ML systems in production
- Proficient in Python and SQL; comfortable with software engineering fundamentals
- Hands-on experience with ETL/ELT orchestration tools and cloud data warehouses
- Familiarity with ML lifecycle tooling such as MLflow, SageMaker, Vertex AI, or similar
- Working knowledge of Docker and Kubernetes and at least one major cloud provider
- Experience with agile development methodologies and version control systems
Nice to Have
- Experience with natural language processing, computer vision, or other areas of machine learning
- Familiarity with cloud-based data platforms such as AWS, GCP, or Azure
- Experience with containerization and orchestration using Docker and Kubernetes
Benefits and Perks
- Competitive salary and equity package
- Comprehensive health, dental, and vision insurance
- Flexible PTO and remote work options
- Professional development opportunities and conference sponsorships
- Access to the latest tools and technologies
- Collaborative and dynamic work environment
How to Stand Out
- Make sure to highlight your experience with machine learning engineering, data engineering, and cloud computing in your resume and cover letter
- Familiarize yourself with Twilio's products and services, and be prepared to discuss how your skills and experience align with the company's goals
- Showcase your ability to work independently and collaboratively as part of a remote team
- Develop a strong understanding of the machine learning lifecycle, including data preparation, model training, and model deployment
- Be prepared to discuss your experience with agile development methodologies and version control systems
- Consider creating a portfolio of your work, including examples of your machine learning models and data visualizations
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