Machine Learning Engineer L4

Summary

Join Twilio as a Staff Machine Learning Engineer to scope, design, and deploy machine learning systems. You will collaborate with Product & Engineering teams, understand customer needs, build global-scale data products, and execute large-scale ML solutions. A deep background in ML engineering and a proven track record of solving data and machine-learning problems at scale are essential. You will build and maintain scalable machine learning solutions, train and validate models, and partner with stakeholders to analyze business problems. The role requires close collaboration with data platform teams and software engineers, upholding high engineering standards through mentoring and knowledge sharing. This remote position is based in India (Karnataka, Tamil Nadu, Telangana State, Maharashtra, and New Delhi).

Requirements

• 7.5+ 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

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

Preferred Qualifications

Experience with Large Language Models

Benefits

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