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; Work closely with software engineers, build tools to enhance productivity and to ship and maintain ML models
Requirements:
5+ years of applied ML experience; Proficiency in Python is preferred. We will also consider strong quantitative candidates with a background in other programming languages; 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; 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
Text:
Machine Learning Engineer 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; Work closely with software engineers, build tools to enhance productivity and to ship and maintain ML models 5+ years of applied ML experience; Proficiency in Python is preferred. We will also consider strong quantitative candidates with a background in other programming languages; 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; 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
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