Capital One
SeniorLeadMachineLearningEngineer
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“Senior Lead Machine Learning Engineer at Capital One. Skills: Machine Learning Engineering, ML model development, ML system design and scaling, Productionizing ML applications. Productionizing machine learning applications and systems at scale. Detailed technical design, development, and implementation of machine learning applications”
What You'll Achieve.
Ensure high availability and performance of our machine learning applications; Ensure all code is well-managed to reduce vulnerabilities; Ensure models are well-governed from a risk perspective; Ensure ML follows best practices in Responsible and Explainable AI; Ensure successful deployment of ML models and application code
Industry & Context.
Solve complex problems
What They're Looking For.
Must Have
Bachelor's Degree, At least 8 years of experience designing and building data-intensive solutions using distributed computing, At least 4 years of experience programming with Python, Scala, or Java, At least 3 years of experience building, scaling, and optimizing ML systems, At least 2 years of experience leading teams developing ML solutions
Nice to Have
Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field, Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform, 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, Kubeflow or TensorFlow, 3+ years of experience developing performant, resilient, and maintainable code, 3+ years of experience with data gathering and preparation for ML models, ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents, 3+ years of experience building production-ready data pipelines that feed ML models, Ability to communicate complex technical concepts clearly to a variety of audiences
What You'll Do.
Productionizing machine learning applications and systems at scale
Detailed technical design
and implementation of machine learning applications
Machine learning architectural design
Develop and review model and application code
Ensure high availability and performance of machine learning applications
and/or deliver ML models and components
Inform ML infrastructure decisions
Solve complex problems by writing and testing application code
Develop and validate ML models
Automate tests and deployment
and monitor models in production
Leverage or build cloud-based architectures
Construct optimized data pipelines to feed ML models
Leverage continuous integration and continuous deployment best practices
How You'll Work.
Team & Collaboration
Work in collaboration with the Product and Data Science teams; Collaborate as part of a cross-functional Agile team
Communication Scope
Communicate complex technical concepts clearly to a variety of audiences
Full Job Description
Senior 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 ensur
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