
location_on2144, Cliffside Drive, Plano, Collin County, Texas, 75023, 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 role is designed for a leader who thrives at the intersection of operations, modeling, and data engineering. You will participate in the detailed technical design, development, and implementation of machine learning applications using both existing and emerging technology platforms. Your focus will be on architectural design, code review, and ensuring the high availability and performance of our ML infrastructure.
In this position, you will collaborate closely with Product and Data Science teams to solve complex business problems. You will be expected to make informed infrastructure decisions regarding model selection, feature engineering, and validation strategies. The role involves constructing optimized data pipelines, leveraging cloud-based architectures, and applying continuous integration and deployment best practices to deliver state-of-the-art big data and ML applications.
Beyond technical execution, you will champion Responsible and Explainable AI, ensuring all code is well-managed to reduce vulnerabilities and that models are governed from a risk perspective. You will have the opportunity to continuously learn and apply the latest innovations in machine learning engineering while retraining and monitoring models in production.
Candidates hired to work in other locations will be subject to the pay range associated with that location. Applications for this role are accepted for a minimum of 5 business days. Please note that Capital One will not sponsor a new applicant for employment authorization or offer immigration-related support for this position.
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
2144, Cliffside Drive, Plano, Collin County, Texas, 75023, United States
Plano, Texas
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 in Computer Science preferred; Doctoral degree in Computer Science preferred.