Machine Learning Engineer

As a Machine Learning Engineer at Twilio, you will build intelligent systems that drive user acquisition, engagement, and retention at scale. You will work at the intersection of data science, engineering, and marketing, transforming insights into real-time decisions and personalized experiences.

• 1Design, build, and deploy machine learning models for growth initiatives
• 2Collaborate with data scientists to translate prototypes into scalable solutions
• 3Work with analysts and product managers to turn business questions into ML solutions
• 4Evaluate and select appropriate algorithms and models for specific tasks
• 5Develop and maintain data pipelines for model training, validation, and deployment
• 6Conduct model testing, versioning, and documentation
• 7Integrate ML models into product and marketing systems via APIs or batch/streaming services
• 8Monitor model performance in production and implement feedback loops
• 9Contribute to experimentation frameworks (e.g., A/B testing infrastructure)
• 10Ensure best practices in model validation, testing, and performance evaluation
• 11Continuously improve existing systems by integrating new data sources and ML techniques
• 12Maintain documentation, testing, and governance around models and datasets
• 13Work closely with stakeholders across various departments to understand business needs
• 14Communicate complex technical concepts to non-technical stakeholders
• 15Stay up-to-date with the latest trends and advancements in machine learning and AI

• Bachelors or Masters degree in Computer Science, Machine Learning, Statistics, or related field
• 2+ years of experience deploying ML models in production environments
• Proficient in Python and ML libraries such as Scikit-learn, TensorFlow, PyTorch, or LightGBM
• Experience with data pipeline tools (e.g., Airflow, dbt) and big data processing (e.g., Spark, Presto)
• Familiarity with cloud-based ML platforms (e.g., AWS SageMaker, Google Vertex AI)
• Proficient in programming languages such as Python, R, or Java
• Solid understanding of statistical methods, machine learning algorithms, and deep learning techniques
• Proven experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, GCP, Azure)
• Strong understanding of experimentation design and metrics relevant to growth
• Comfortable working in a fast-paced, collaborative environment focused on measurable impact