Capital One
LeadMachineLearningEngineer
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optimal for Lead candidates.
“Lead Machine Learning Engineer at Capital One. Skills: Machine Learning Engineering, ML Systems, Cloud Architecture, Data Pipelines. Productionize ML applications. Design ML applications”
Industry & Context.
Solve complex problems
No sponsorship for employment authorization
What They're Looking For.
Must Have
Bachelor's degree, 6 years of experience designing and building data-intensive solutions using distributed computing, 4 years of experience programming with Python, Scala, or Java, 2 years of experience building, scaling, and optimizing ML systems
Nice to Have
Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field, 3+ years of experience building production-ready data pipelines that feed ML models, 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow, 2+ years of experience developing performant, resilient, and maintainable code, 2+ years of experience with data gathering and preparation for ML models, 2+ years of people leader experience, 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation, Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform, Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance, ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
What You'll Do.
Productionize ML applications
Design ML applications
Develop ML applications
Implement ML applications
Design ML architecture
Ensure high availability
Write application code
Test application code
Leverage cloud architectures
Build cloud architectures
Deliver optimized models
Construct data pipelines
Ensure code management
Ensure model governance
Follow AI best practices
How You'll Work.
Team & Collaboration
Work in collaboration with Product and Data Science teams; Collaborate as part of a cross-functional Agile team
Full Job Description
Lead Machine Learning Engineer 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. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. ****What you’ll do in the role:**** * 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 one or more of the following: * Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. * 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). * Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. * Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. * Retrain, maintain, and monitor models in production. * Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. * Construct optimized data pipelines to feed ML models. * Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful d
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