Senior Machine Learning Engineer (Python, Spark/Dask, MLOPS)
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About The Position
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. Your focus will be on machine learning architectural design, developing and reviewing model and application code, and ensuring high availability and performance of our machine learning applications. This role offers the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including designing, building, and delivering ML models and components that solve real-world business problems, while collaborating with the Product and Data Science teams. You will inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation. You will solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. Collaboration as part of a cross-functional Agile team will be essential to create and enhance software that enables state-of-the-art big data and ML applications. Additionally, you will retrain, maintain, and monitor models in production, leverage or build cloud-based architectures, and construct optimized data pipelines to feed ML models. You will also leverage continuous integration and continuous deployment best practices, ensuring all code is well-managed to reduce vulnerabilities and that models are well-governed from a risk perspective, following best practices in Responsible and Explainable AI.
