Lead Machine Learning Engineer (Enterprise Platforms Technology)
Not Disclosed•Full-TimeOn-site
location_onFairfax County Fire Station Number 1, McLean, 1455, Laughlin Avenue, Bryn Mawr, McLean, Fairfax County, Virginia, 22101, 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 advanced data science and real-world business impact by building robust, high-performance ML infrastructure. We foster a culture of continuous learning, encouraging engineers to apply the latest innovations and best practices in machine learning engineering to solve complex challenges.
This role sits at the intersection of Operations, Modeling, and Data Engineering. You will be responsible for the end-to-end lifecycle of machine learning solutions, from detailed technical design and development to deployment and monitoring. Your work will directly influence how Capital One leverages big data and ML to drive decision-making. You will collaborate closely with Product and Data Science teams to architect solutions that are not only performant but also governed for risk and aligned with Responsible and Explainable AI principles. The day-to-day involves writing application code, optimizing data pipelines, and ensuring the high availability of ML systems in a cloud-based environment.
We are committed to a fair and efficient hiring process. Applications for this role are accepted for a minimum of 5 business days. Please note that no agencies are accepted for this position. 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 necessary 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 qualified applicants with a criminal history in a manner consistent with relevant laws. We strive to create an inclusive environment where diverse backgrounds and perspectives are valued.
At this time, Capital One will not sponsor a new applicant for employment authorization or offer immigration-related support for this position. Salary information is provided for transparency regarding the specific locations listed (McLean, VA and New York, NY) and is subject to change based on the final offer. This role is eligible for performance-based incentive compensation.
Work model: On-site
Fairfax County Fire Station Number 1, McLean, 1455, Laughlin Avenue, Bryn Mawr, McLean, Fairfax County, Virginia, 22101, United States
McLean, Virginia
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 an industry recognized ML framework 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 people leader experience. 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation. Experience developing and 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.
Skills: Machine Learning, Python, Scala, Java, Distributed Computing, Scikit-Learn, Pytorch, Dask, Spark, Tensorflow.
Education: Bachelor's Degree required; Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or similar field preferred.