Job
Description
As a Staff Engineer specializing in Machine Learning and Feature Engineering for the Identity Resolution team, you will drive the development of sophisticated ML models and advanced feature engineering techniques designed to enhance our identity resolution capabilities. Your work will play a critical role in improving our systems ability to match and unify customer identities accurately, enabling more personalized customer experiences and strategic business insights. Your role will also involve building robust data infrastructure to support and scale our machine learning initiatives. To thrive in this role, you must have a deep background in ML engineering, and a consistent track record of solving data & machine-learning problems at scale. You are a self-starter, embody a growth attitude, and collaborate effectively across the entire Twilio organization Responsibilities Design, implement, and refine machine learning models that improve the precision and recall of identity resolution algorithms. Develop and optimize feature engineering methodologies to extract meaningful patterns from large and complex datasets that enhance identity matching and unification. Develop and maintain scalable data infrastructure to support the deployment and training of machine learning models, ensuring that they run efficiently under varying loads. Build and maintain scalable machine learning solutions in production Train and validate both deep learning-based and statistical-based models considering use-case, complexity, performance, and robustness Demonstrate end-to-end understanding of applications and develop a deep understanding of the why behind our models & systems Partner with product managers, tech leads, and stakeholders to analyze business problems, clarify requirements and define the scope of the systems needed Ensure high standards of operational excellence by implementing efficient processes, monitoring system performance, and proactively addressing potential issues. Drive engineering best practices around code reviews, automated testing and monitoring Qualifications *Required: 7+ years of applied ML experience. Proficiency in Python, Java or Golang is preferred. Extensive experience in feature engineering and developing data-driven frameworks that enhance identity matching algorithms. Strong background in the foundations of machine learning and building blocks of modern deep learning Deep understanding of machine learning frameworks and libraries such as TensorFlow, PyTorch, or Scikit-learn. Experience with big data technologies like Apache Spark or Hadoop, and familiarity with cloud platforms (AWS, Azure, Google Cloud) for scalable data processing. Familiarity with ML Ops concepts related to testing and maintaining models in production such as testing, retraining, and monitoring. Experienced with modern data storage, messaging, and processing tools (Kafka, Apache Spark, Hadoop, Presto, DynamoDB etc.) and demonstrated experience designing and coding in big-data components such as DynamoDB or similar Experience working in an agile team environment with changing priorities Experience of working on AWS Desired: Exposure to Advertising Technology, Marketing Technology domains. Experience designing and implementing highly available, performant, and fault-tolerant distributed systems that provide durable and (eventually) consistent results. Experience with Large Language Models
