Capital One
Lead Machine Learning Engineer (ML Algorithms, Deep Learning, Python, AWS)
Capital One, Mc Lean, Virginia, us, 22107
Overview
Lead Machine Learning Engineer (ML Algorithms, Deep Learning, Python, AWS) at Capital One. The role focuses on productionizing machine learning applications and systems at scale, participating in technical design, development, and implementation of ML applications, and ensuring high availability and performance. The role offers opportunities to apply the latest innovations and best practices in ML engineering. What you’ll do in the role
Collaborate in a cross-functional Agile team to design, build, and deliver ML models and components that solve real-world business problems, in partnership with Product and Data Science teams. Inform ML infrastructure decisions using understanding of modeling techniques, data, feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation. Develop and test application code, build ML models, and automate tests and deployment to solve complex problems. Retrain, maintain, and monitor models in production; construct optimized data pipelines for ML models. Leverage or build cloud-based architectures and platforms to deliver optimized ML models at scale. Apply continuous integration and continuous deployment practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure code quality and governance, with responsible and explainable AI practices. Use programming languages such as Python, Scala, or Java. Basic Qualifications
Bachelor’s degree At least 6 years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply) At least 4 years of experience programming with Python, Scala, or Java At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications
Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a related field 3+ years of experience building production-ready data pipelines that feed ML models 3+ years of experience with ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, maintainable code 2+ years of experience with data gathering and preparation for ML models 2+ years of people leadership experience 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation Experience 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 Note:
Capital One is an equal opportunity employer. No sponsorship information is included here beyond standard postings. This role is eligible for performance-based incentives and benefits as described in Capital One’s careers materials. Salary ranges are location-based and provided for transparency; actual offer details are provided in the offer letter.
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Lead Machine Learning Engineer (ML Algorithms, Deep Learning, Python, AWS) at Capital One. The role focuses on productionizing machine learning applications and systems at scale, participating in technical design, development, and implementation of ML applications, and ensuring high availability and performance. The role offers opportunities to apply the latest innovations and best practices in ML engineering. What you’ll do in the role
Collaborate in a cross-functional Agile team to design, build, and deliver ML models and components that solve real-world business problems, in partnership with Product and Data Science teams. Inform ML infrastructure decisions using understanding of modeling techniques, data, feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation. Develop and test application code, build ML models, and automate tests and deployment to solve complex problems. Retrain, maintain, and monitor models in production; construct optimized data pipelines for ML models. Leverage or build cloud-based architectures and platforms to deliver optimized ML models at scale. Apply continuous integration and continuous deployment practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure code quality and governance, with responsible and explainable AI practices. Use programming languages such as Python, Scala, or Java. Basic Qualifications
Bachelor’s degree At least 6 years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply) At least 4 years of experience programming with Python, Scala, or Java At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications
Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a related field 3+ years of experience building production-ready data pipelines that feed ML models 3+ years of experience with ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, maintainable code 2+ years of experience with data gathering and preparation for ML models 2+ years of people leadership experience 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation Experience 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 Note:
Capital One is an equal opportunity employer. No sponsorship information is included here beyond standard postings. This role is eligible for performance-based incentives and benefits as described in Capital One’s careers materials. Salary ranges are location-based and provided for transparency; actual offer details are provided in the offer letter.
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