Lead Machine Learning Engineer
Not Disclosed•Full-TimeOn-site
location_onGrace Street Commercial Historic District, Byrd Street Cycle Track, Richmond, Virginia, 23284, 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 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 we leverage big data and ML to drive state-of-the-art applications. You will collaborate closely with Product and Data Science teams to design, build, and deliver ML models that solve real-world business problems. Beyond coding, you will inform critical infrastructure decisions regarding model selection, feature engineering, and validation strategies, ensuring that our systems are optimized, scalable, and governed by principles of Responsible and Explainable AI.
We are committed to a fair and efficient hiring process. Applications for this role will be accepted for a minimum of 5 business days. We encourage you to review the requirements and qualifications carefully before applying. Please note that no agencies are permitted to submit candidates 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.
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 any immigration related support for this position. Additionally, Capital One does not provide, endorse, nor guarantee third-party products, services, or educational tools available through this site.
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. 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.