Staff Machine Learning Engineer

This position is needed to scope, design, and deploy machine learning systems into the real world, the individual will closely partner with Product & Engineering teams to execute the roadmap for Twilio’s AI/ML products and services. You will understand customers need, build data products that works at a global scale and own end-to-end execution of large scale ML solutions. 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 organizations.

Responsibilities

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; Work closely with data platform teams to build robust scalable batch and realtime data pipelines; Collaborate with software engineers, build tools to enhance productivity and to ship and maintain ML models; Drive high engineering standards on the team through mentoring and knowledge sharing; Uphold engineering best practices around code reviews, automated testing and monitoring

Requirements

7+ years of applied ML experience with proficiency in Python; Strong background in the foundations of Machine Learning and building blocks of modern Deep Learning; Track record of building, shipping and maintaining Machine Learning models in production in an ambiguous and fast paced environment; Track record of designing and architecting large scale experiments and analysis to inform product roadmap; You have a clear understanding of frameworks like - PyTorch, TensorFlow, or Keras, why and how these frameworks do what they do; Familiarity with ML Ops concepts related to testing and maintaining models in production such as testing, retraining, and monitoring; Demonstrated ability to ramp up, understand, and operate effectively in new application / business domains; You’ve explored 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

Nice to have

Experience with Large Language Models

Benefits

competitive pay; generous time off; ample parental and wellness leave; healthcare; retirement savings program