Model optimization: collaborate with data scientists to improve existing machine learning model training and evaluation pipelines, optimize the model training pipeline speed for faster iteration; Model Deployment: optimize the model inferencing performance through quantization and model conversion, define and leverage appropriate resources for model hosting and inferencing; Inference Pipeline product prioritization: work with data scientists and data engineers to design and implement the data pipelines for machine learning models that will support the current and future needs of our business; Service Deployment: build continuous integration, testing, and scalable deployment pipelines in cloud computing environments for machine learning services
Requirements:
BS or above in Computer Science, Software Engineering, Data Science, or a related field; 2 years of industry experience building ML infrastructure at scale; 1 year of experience in developing and deploying large-scale systems, version control, scaling and monitoring; Experience in Machine Learning frameworks (scikit-learn, Tensorflow, Pytorch), big data frameworks (Spark/Hadoop/Flink), and experience in resource management and task scheduling for large-scale distributed systems; Proficient in Python/SQL and of C++/Go, with deep knowledge of Linux and CD tools (e.g. Git)
Text:
Machine Learning Engineer - Applied AI Model optimization: collaborate with data scientists to improve existing machine learning model training and evaluation pipelines, optimize the model training pipeline speed for faster iteration; Model Deployment: optimize the model inferencing performance through quantization and model conversion, define and leverage appropriate resources for model hosting and inferencing; Inference Pipeline product prioritization: work with data scientists and data engineers to design and implement the data pipelines for machine learning models that will support the current and future needs of our business; Service Deployment: build continuous integration, testing, and scalable deployment pipelines in cloud computing environments for machine learning services BS or above in Computer Science, Software Engineering, Data Science, or a related field; 2 years of industry experience building ML infrastructure at scale; 1 year of experience in developing and deploying large-scale systems, version control, scaling and monitoring; Experience in Machine Learning frameworks (scikit-learn, Tensorflow, Pytorch), big data frameworks (Spark/Hadoop/Flink), and experience in resource management and task scheduling for large-scale distributed systems; Proficient in Python/SQL and of C++/Go, with deep knowledge of Linux and CD tools (e.g. Git)
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