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

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

Staff Machine Learning Engineer – L4

Key skills