Capital One
Overview
Lead Machine Learning role at Capital One. The role focuses on building models and productionizing machine learning applications and systems at scale, including Kafka infrastructure for event-driven architecture, DevOps practices, and tools to facilitate cloud-based development. You will participate in detailed technical design, development, and implementation of machine learning applications, with emphasis on architectural design, code review, and ensuring high availability and performance. There is a focus on learning and applying the latest innovations in machine learning engineering. What you’ll do in the role
The MLE role overlaps with Operations, Modeling, and Data Engineering. You will perform ML engineering activities, including one or more of the following: design, build, and deliver ML models and components in collaboration with Product and Data Science teams. Inform ML infrastructure decisions using understanding of modeling techniques, 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 in a cross-functional Agile team to create and enhance software for state-of-the-art big data and ML applications. Retrain, maintain, and monitor models in production. Leverage or build cloud-based architectures and platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure code is well-managed to reduce vulnerabilities, ensure model governance from a risk perspective, and follow Responsible and Explainable AI practices. Use programming languages such as Python, Scala, or Java. Guide early-career engineers with learning tasks and work-related tasks, mentor to help grow technical skillsets. Operate independently, investigate solutions deeply, and see tasks through from planning and design to deployment and adoption. Adapt to changing priorities, communicate complex ideas clearly, and actively listen to feedback. Use critical thinking to question assumptions, improve processes, and learn new technologies, create proofs-of-concept, and educate others. 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 similar field 3+ years of experience building production-ready data pipelines that feed ML models 3+ years of on-the-job experience with ML frameworks 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 experience leading teams developing ML solutions using industry best practices, patterns, and automation 2+ years of experience developing and deploying ML solutions in a public cloud (AWS, Azure, or GCP) 2+ years experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance; 2+ years experience with event-driven architectures such as Kafka Other information
At this time, Capital One will not sponsor a new applicant for employment authorization or offer immigration-related support for this position. Salaries vary by location and are described in the offer letter. This role is eligible for performance-based incentives. Capital One offers a comprehensive benefits package. This role is expected to accept applications for a minimum of 5 business days. Capital One is an equal opportunity employer (EOE, including disability/vet) and maintains a drug-free workplace. For accommodations, contact Recruiting at 1-800-304-9102 or RecruitingAccommodation@capitalone.com. For technical questions about the recruiting process, contact Careers@capitalone.com. Capital One does not endorse or guarantee third-party products or services accessed through this site. For roles posted in other countries, refer to the respective Capital One entities.
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Lead Machine Learning role at Capital One. The role focuses on building models and productionizing machine learning applications and systems at scale, including Kafka infrastructure for event-driven architecture, DevOps practices, and tools to facilitate cloud-based development. You will participate in detailed technical design, development, and implementation of machine learning applications, with emphasis on architectural design, code review, and ensuring high availability and performance. There is a focus on learning and applying the latest innovations in machine learning engineering. What you’ll do in the role
The MLE role overlaps with Operations, Modeling, and Data Engineering. You will perform ML engineering activities, including one or more of the following: design, build, and deliver ML models and components in collaboration with Product and Data Science teams. Inform ML infrastructure decisions using understanding of modeling techniques, 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 in a cross-functional Agile team to create and enhance software for state-of-the-art big data and ML applications. Retrain, maintain, and monitor models in production. Leverage or build cloud-based architectures and platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure code is well-managed to reduce vulnerabilities, ensure model governance from a risk perspective, and follow Responsible and Explainable AI practices. Use programming languages such as Python, Scala, or Java. Guide early-career engineers with learning tasks and work-related tasks, mentor to help grow technical skillsets. Operate independently, investigate solutions deeply, and see tasks through from planning and design to deployment and adoption. Adapt to changing priorities, communicate complex ideas clearly, and actively listen to feedback. Use critical thinking to question assumptions, improve processes, and learn new technologies, create proofs-of-concept, and educate others. 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 similar field 3+ years of experience building production-ready data pipelines that feed ML models 3+ years of on-the-job experience with ML frameworks 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 experience leading teams developing ML solutions using industry best practices, patterns, and automation 2+ years of experience developing and deploying ML solutions in a public cloud (AWS, Azure, or GCP) 2+ years experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance; 2+ years experience with event-driven architectures such as Kafka Other information
At this time, Capital One will not sponsor a new applicant for employment authorization or offer immigration-related support for this position. Salaries vary by location and are described in the offer letter. This role is eligible for performance-based incentives. Capital One offers a comprehensive benefits package. This role is expected to accept applications for a minimum of 5 business days. Capital One is an equal opportunity employer (EOE, including disability/vet) and maintains a drug-free workplace. For accommodations, contact Recruiting at 1-800-304-9102 or RecruitingAccommodation@capitalone.com. For technical questions about the recruiting process, contact Careers@capitalone.com. Capital One does not endorse or guarantee third-party products or services accessed through this site. For roles posted in other countries, refer to the respective Capital One entities.
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