Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS)
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
The Intelligent Foundations and Experiences (IFX) team sits at the center of Capital One's vision for AI. We work hand-in-hand with partners across the company to advance the state of the art in science and AI engineering. Our mission is to build and deploy proprietary solutions that are central to the business and deliver value to millions of customers. By empowering teams across Capital One to enhance their products with the transformative power of AI, we aim to create responsible and scalable solutions for the highest leverage impact.
As a Lead Machine Learning Engineer within an Agile team, you will be dedicated to productionizing machine learning applications and systems at scale. This role is a hybrid of operations, modeling, and data engineering, requiring you to participate in the detailed technical design, development, and implementation of ML applications using existing and emerging technology platforms.
You will focus on machine learning architectural design, developing and reviewing model and application code, and ensuring high availability and performance. In this position, you will collaborate with Product and Data Science teams to solve complex problems by writing and testing application code, automating tests, and deploying optimized ML models. You will also be responsible for retraining, maintaining, and monitoring models in production, ensuring all code is well-managed to reduce vulnerabilities and that models adhere to best practices in Responsible and Explainable AI.
Candidates are expected to go through a standard interview process which typically includes an initial screening, technical deep-dives, and team fit assessments. For specific details on the current stage of the hiring cycle, please refer to the application portal.
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.
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 and used only to the extent required to provide needed reasonable accommodations.
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.
Capital One • Richmond, Virginia
Capital One • New York, New York
Capital One • New York, New York
Skills: Machine Learning, Mlops, Kserve, Kubernetes, Pytorch, Tensorflow, Aws, Python, Scala, Java.
Education: Bachelor's degree required; Master's or doctoral degree in computer science, electrical engineering, mathematics, or similar field preferred.