
location_onNYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, 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 technical 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 problems. You will be expected to inform infrastructure decisions based on a deep understanding of modeling techniques, including feature selection, hyperparameter tuning, and bias/variance trade-offs. The role involves retraining, maintaining, and monitoring models in production while leveraging cloud-based architectures to deliver optimized solutions. You will also champion best practices in Responsible and Explainable AI, ensuring all code is well-managed and models are governed from a risk perspective.
Candidates hired to work in other locations will be subject to the pay range associated with that location. The actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.
This role is expected to accept applications for a minimum of 5 business days. No agencies please.
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.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well-being. We are committed to providing a supportive environment where you can continuously learn and apply the latest innovations in machine learning engineering.
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).
If you require an accommodation to apply for a position, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential.
Work model: On-site
NYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
New York, New York
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 degree in Computer Science preferred; Doctoral degree in Computer Science preferred.