Build and maintain scalable, high quality machine learning solutions in production; Design and implement tools and procedures to evaluate performance and accuracy of models and data; Work closely with software engineers, build tools to enhance productivity and to ship and maintain ML models; Demonstrate end-to-end understanding of applications and “why” behind models & systems and develop high quality ML-based software at scale; Truly own the product you work on. Be responsible for SLA, on call, incident resolution, customer feedback, and participate in blameless post-mortems to make our products better
Requirements:
3+ years of applied ML experience; Strong background in the foundations of machine learning and building blocks of modern deep learning; Proficiency in Python or Java and familiarity with design patterns; Track record of building, shipping and maintaining machine learning models in production; Extensive experience in technologies such as PyTorch, Tensorflow, Scikit-learn, Spacy, NLTK, application frameworks (e.g., Flask); Deep understanding of ML model implementation cycle (e.g., feature engineering, training/serving, A/B test, model selection, etc.), algorithms (e.g., xgboost, time series models, deep learning, graph neural networks, optimization) and domains (e.g., forecasting, personalization and recommendation system, NLP, embedding representation)
Text:
Machine Learning Engineer Build and maintain scalable, high quality machine learning solutions in production; Design and implement tools and procedures to evaluate performance and accuracy of models and data; Work closely with software engineers, build tools to enhance productivity and to ship and maintain ML models; Demonstrate end-to-end understanding of applications and “why” behind models & systems and develop high quality ML-based software at scale; Truly own the product you work on. Be responsible for SLA, on call, incident resolution, customer feedback, and participate in blameless post-mortems to make our products better 3+ years of applied ML experience; Strong background in the foundations of machine learning and building blocks of modern deep learning; Proficiency in Python or Java and familiarity with design patterns; Track record of building, shipping and maintaining machine learning models in production; Extensive experience in technologies such as PyTorch, Tensorflow, Scikit-learn, Spacy, NLTK, application frameworks (e.g., Flask); Deep understanding of ML model implementation cycle (e.g., feature engineering, training/serving, A/B test, model selection, etc.), algorithms (e.g., xgboost, time series models, deep learning, graph neural networks, optimization) and domains (e.g., forecasting, personalization and recommendation system, NLP, embedding representation)
Please click here, if the job didn't load correctly.
Please wait. You are being redirected to the job in 3 seconds.
