About the job
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
Required Qualifications
- 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
Desired Qualifications
- Experience with Large Language Models