
location_onNYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices.
As a Capital One Machine Learning Engineer (MLE), you will be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. This team focuses on the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms, ensuring high availability and performance.
The Sr. Lead Machine Learning Engineer role sits at the intersection of Operations, Modeling, and Data Engineering. You will collaborate with Product and Data Science teams to design, build, and deliver ML models and components that solve real-world business problems. Your day-to-day involves making critical infrastructure decisions based on your understanding of ML modeling techniques, including model selection, feature engineering, and validation strategies.
In this position, you will lead the creation and enhancement of software that enables state-of-the-art big data and ML applications. You will be responsible for constructing optimized data pipelines, leveraging cloud-based architectures to deliver models at scale, and utilizing continuous integration and deployment best practices. A key part of your mission is to ensure all code is well-managed, models are well-governed from a risk perspective, and the ML engineering follows best practices in Responsible and Explainable AI.
As a leader, you will guide the team in retraining, maintaining, and monitoring models in production, while continuously learning and applying the latest innovations in machine learning engineering.
Capital One is committed to a fair and efficient hiring process. We expect to accept applications for a minimum of 5 business days. Please note that no agencies are accepted for this role.
If you require an accommodation during the recruiting process, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information provided will be kept confidential and used only to the extent required to provide needed reasonable accommodations.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. We promote a drug-free workplace and consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws.
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. We offer a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well-being.
Skills: Machine Learning, Python, Scala, Java, Aws, Azure, Google Cloud Platform, Scikit-Learn, Pytorch, Dask.
Education: Bachelor's degree required; Master's or doctoral degree in computer science, electrical engineering, mathematics, or similar field preferred.
Work model: On-site
NYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
New York, New York
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, or TensorFlow. 3+ years of experience developing performant, resilient, and maintainable code. 3+ years of experience with data gathering and preparation for ML models. 3+ years of people management experience. 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.
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