
location_onGrace Street Commercial Historic District, Byrd Street Cycle Track, Richmond, Virginia, 23284, United States
As a Capital One Machine Learning Engineer, 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 not only accurate but also robust, scalable, and secure.
This Senior Lead position sits at the intersection of Operations, Modeling, and Data Engineering. You will be responsible for the full lifecycle of machine learning systems, from technical design and development to deployment and monitoring. The role requires a deep understanding of ML modeling techniques to inform infrastructure decisions, including model selection, feature engineering, and validation strategies.
In this capacity, you will lead cross-functional efforts to create state-of-the-art big data and ML applications. You will work closely with Product and Data Science teams to solve complex business problems, construct optimized data pipelines, and leverage cloud-based architectures to deliver models at scale. A key part of your day-to-day involves ensuring high availability, performance, and governance of ML applications, with a strong emphasis on Responsible and Explainable AI.
As a leader, you will guide teams in developing resilient code, automating testing and deployment, and maintaining models in production. You will also champion the adoption of the latest innovations and best practices in machine learning engineering, fostering a culture of continuous learning and technical excellence.
Capital One is committed to a fair and transparent hiring process. We encourage all qualified candidates to apply. Please note that this role is expected to accept applications for a minimum of 5 business days. We do not accept unsolicited resumes from agencies.
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 solely to facilitate your accommodation needs.
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 applicable laws.
We foster an inclusive culture where diverse backgrounds and perspectives are valued. Capital One will consider sponsoring a new qualified applicant for employment authorization for this position where applicable.
Work model: On-site
Grace Street Commercial Historic District, Byrd Street Cycle Track, Richmond, Virginia, 23284, United States
Richmond, Virginia
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.
Capital One • New York, New York
Capital One • McLean, Virginia
Capital One • McLean, 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, electrical engineering, mathematics, or similar field preferred.