
location_onNYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
As a Capital One Machine Learning Engineer (MLE), you will join an Agile team dedicated to productionizing machine learning applications and systems at scale. Our mission is to bridge the gap between data science innovation and real-world business impact, ensuring that machine learning solutions are robust, scalable, and ready for the demands of a financial services environment.
This Senior Lead position sits at the intersection of Operations, Modeling, and Data Engineering. You will be responsible for the end-to-end lifecycle of machine learning applications, from detailed technical design and development to the implementation of high-availability systems. Your work will involve architecting ML solutions, reviewing model and application code, and continuously integrating the latest innovations and best practices in the field.
In this role, you will collaborate closely with Product and Data Science teams to solve complex, real-world business problems. You will guide infrastructure decisions based on deep technical understanding of modeling techniques, data selection, and validation strategies. Beyond building and optimizing models, you will lead the effort to maintain, monitor, and retrain models in production, ensuring they remain performant and resilient. A key part of your leadership will be fostering a culture of Responsible and Explainable AI, ensuring all code is well-managed and models are governed from a risk perspective.
Candidates hired to work in other locations will be subject to the pay range associated with that location. Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. Please note that this role is expected to accept applications for a minimum of 5 business days. No agencies please.
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.
We offer a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well-being. If you require an accommodation to apply or participate in 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.
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. Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion.
Capital One • New York, New York
Capital One • McLean, Virginia
Capital One • Richmond, Virginia
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 preferred; Doctoral Degree in computer science preferred.