Machine Learning Engineer

Summary

Join Twilio as a Machine Learning Engineer to build intelligent systems driving user acquisition, engagement, and retention. You will work at the intersection of data science, engineering, and marketing, transforming insights into real-time decisions and personalized experiences. As a member of the Revenue and Growth Marketing department, you will lead the development and building of data products and advanced machine learning models to support business growth. You will collaborate with cross-functional teams to achieve business outcomes through data, contributing to strategic objectives. This role requires designing, building, and deploying machine learning models for various growth initiatives, collaborating with data scientists and product managers, and ensuring model scalability and efficiency. You will also develop and maintain data pipelines, integrate ML models into systems, monitor performance, and contribute to experimentation frameworks. The position demands strong communication skills to convey complex technical concepts to non-technical stakeholders.

Requirements

• Bachelor's or Master’s 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 and tools for model deployment (e.g., MLflow, Kubernetes, Docker, Metaflow)
• 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 (e.g., conversion rate, LTV)
• Comfortable working in a fast-paced, collaborative environment focused on measurable impact

Responsibilities

• Design, build, and deploy machine learning models that power growth initiatives, such as customer segmentation, churn prediction, personalization, campaign optimization, and recommendation systems
• Collaborate with data scientists to translate prototypes into scalable solutions
• Collaborate with analysts and product managers to turn business questions into measurable ML solutions
• Evaluate and select appropriate algorithms and models for specific tasks, ensuring scalability and efficiency
• Develop and maintain data pipelines for model training, validation, and deployment
• Develop scalable data and ML pipelines using best-in-class tools and practices (e.g., Airflow, Spark, MLflow)
• Conduct model testing, versioning, and documentation to ensure reproducibility and maintainability
• Integrate ML models into product and marketing systems via APIs or batch/streaming services
• Monitor model performance in production and implement feedback loops for continuous learning
• Contribute to experimentation frameworks (e.g., A/B testing infrastructure) to evaluate ML-driven features
• Ensure best practices in model validation, testing, and performance evaluation
• Continuously improve existing systems by integrating new data sources and ML techniques
• Maintain documentation, testing, and governance around models and datasets to ensure reliability and transparency
• Work closely with stakeholders across various departments (e.g., Marketing, Sales, Product, R&D) to understand business needs and translate them into data science and machine learning solutions
• Communicate complex technical concepts to non-technical stakeholders clearly and effectively
• Stay up-to-date with the latest trends and advancements in machine learning and AI, and integrate new techniques into the team's workflow

Preferred Qualifications

• Experience with user behavior modeling, marketing attribution, or real-time decision systems
• Understanding of MLOps practices, including CI/CD for ML, model monitoring, and retraining pipelines
• Familiarity with communication channels and platforms (e.g., push, email, in-app messaging)

Benefits

• Competitive pay
• Generous time off
• Ample parental and wellness leave
• Healthcare
• A retirement savings program