Twilio

Machine Learning Engineer

Twilio

full-time

Posted on:

Origin:  • 🇨🇦 Canada

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Job Level

JuniorMid-Level

Tech Stack

AirflowAWSAzureCloudDockerGoogle Cloud PlatformHadoopJavaKubernetesPythonPyTorchScikit-LearnSparkTensorflow

About the role

  • 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.

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.