Machine Learning Engineer

Summary

Join Twilio as a 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. This role requires a deep background in ML engineering and a proven track record of solving data and machine-learning problems at scale. You will build algorithms, transform data science prototypes, work with ML engineers to enhance productivity, conduct EDA, manage infrastructure, demonstrate end-to-end application understanding, partner with product managers, use AWS, automate processes, and drive high engineering standards. The position is remote-based in India (Karnataka, Tamil Nadu, Telangana State, Maharashtra, and New Delhi) with occasional travel.

Requirements

• 2 - 5 years of applied ML experience in statistical and mathematical modeling such as supervised and unsupervised machine learning, deep learning
• Strong proficiency in Python to effectively analyze data and solve complex technical challenges
• Track record of building, shipping and maintaining machine learning systems in a highly ambiguous and fast paced environment
• You have a clear understanding of frameworks like - PyTorch, TensorFlow, or Keras, why and how these frameworks do what they do
• Familiarity with concepts related to testing and maintaining models in production such as A/B testing, retraining, monitoring model performance
• 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

Responsibilities

• Build algorithms based on statistical modeling procedures and maintain scalable machine learning solutions in production
• Transform data science prototypes and applying appropriate ML algorithms and tools
• Work closely with the ML Engineers, build tools to enhance their productivity and to ship and maintain ML models
• Conduct exploratory data analysis (EDA) on large-scale datasets to identify patterns and extract features that directly support the problem statement
• Manage the infrastructure and data pipelines needed to bring code to production
• Demonstrate end-to-end understanding of applications (including, but not limited to, the machine learning algorithms) being created
• Partner with product managers and architects to analyze business problems, clarify requirements and define the scope of the systems needed
• Use cloud platform AWS to handle larger scale data
• Support operational leaders by developing code to automate manual processes
• Drive high engineering standards on the team through code review, automated testing, and mentoring

Preferred Qualifications

Experience with Large Language Models

Benefits

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