Staff Machine Learning Engineer (L4)
T
TwilioCommunications
IndiaFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSPythonDynamoDBHadoopKafkaKerasMachine LearningPyTorchSparkTensorflowDeep LearningMLOps
Requirements
- 7+ years of applied ML experience
- 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
- Track record of designing and architecting large scale experiments and analysis to inform product roadmap
- Clear understanding of frameworks like PyTorch, TensorFlow, or Keras
- 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
- Experience with modern data storage, messaging, and processing tools (Kafka, Apache Spark, Hadoop, Presto, DynamoDB etc.)
- 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
- Experience with Large Language Models (desired)
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
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