Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS)
Not Disclosed•Full-TimeOn-site
location_onGrace Street Commercial Historic District, Byrd Street Cycle Track, Richmond, Virginia, 23284, United States
About the Team
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 our business, delivering 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 with the highest leverage impact.
About the Role
As a Lead Machine Learning Engineer, you will join an Agile team dedicated to productionizing machine learning applications and systems at scale. This role serves as a critical bridge between Ops, Modeling, and Data Engineering disciplines. You will participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms.
In this position, you will focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our ML applications. You will have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering, solving complex problems by automating tests, deployment, and model retraining while ensuring responsible and explainable AI practices.
Hiring Process
Candidates are expected to submit applications for a minimum of 5 business days. Please note that no agencies will be accepted for this position. For any questions regarding the recruiting process or technical support, please contact Careers@capitalone.com.
Equal Opportunity & Culture
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 applicable laws. If you require an accommodation to apply or interview, 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 location
Work model: On-site
Grace Street Commercial Historic District, Byrd Street Cycle Track, Richmond, Virginia, 23284, United States
Richmond, Virginia
Key Responsibilities
- check_circleDesign, build, and deliver ML models and components to solve real-world business problems
- check_circleDevelop and review model and application code using Python, Scala, or Java
- check_circleConstruct optimized data pipelines to feed ML models
- check_circleLeverage cloud-based architectures to deliver optimized ML models at scale
- check_circleImplement continuous integration and deployment best practices for ML applications
- check_circleEnsure code is well-managed and models follow Responsible and Explainable AI best practices
- check_circleRetrain, maintain, and monitor models in production environments
Requirements
- verifiedBachelor's degree
- verified6+ years designing data-intensive solutions using distributed computing
- verified4+ years programming with Python, Scala, or Java
- verified2+ years building, scaling, and optimizing ML systems
- verifiedMaster's or doctoral degree in computer science, electrical engineering, mathematics, or similar field (preferred)
- verified3+ years building production-ready data pipelines for ML models (preferred)
- verified3+ years experience with ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow) (preferred)
- verified2+ years developing performant, resilient, and maintainable code (preferred)
- verified2+ years experience with data gathering and preparation for ML models (preferred)
- verified
Nice to Have
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.
Benefits & Perks
check_circlePerformance-based incentive compensation including cash bonuses and/or long term incentives
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Skills, education and keywords
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.
Frequently asked questions about Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) at Capital One
What does a Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) at Capital One do?expand_more
Day-to-day, the Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) at Capital One will design, build, and deliver ml models and components to solve real-world business problems; develop and review model and application code using python, scala, or java; construct optimized data pipelines to feed ml models; and leverage cloud-based architectures to deliver optimized ml models at scale.
What are the requirements for this Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) role?expand_more
Capital One is looking for candidates who meet the following requirements: Bachelor's degree; 6+ years designing data-intensive solutions using distributed computing; 4+ years programming with Python, Scala, or Java; 2+ years building, scaling, and optimizing ML systems; Master's or doctoral degree in computer science, electrical engineering, mathematics, or similar field (preferred); and 3+ years building production-ready data pipelines for ML models (preferred).
Where is the Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) role at Capital One located?expand_more
Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) at Capital One is based in Grace Street Commercial Historic District, Byrd Street Cycle Track, Richmond, Virginia, 23284, United States. This is a on-site role.
Is this Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) job remote, hybrid, or on-site?expand_more
Capital One has listed this Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) role as on-site.
How much experience is required for this Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) role?expand_more
Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) at Capital One typically requires 6+ years of relevant experience at the lead level.
What skills do you need for the Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) role at Capital One?expand_more
Key skills for Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) at Capital One include Machine Learning; Mlops; Kserve; Kubernetes; Pytorch; Tensorflow; Aws; and Python.
What education is required for Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) at Capital One?expand_more
Educational requirements for this role: Bachelor's degree required; and Master's or doctoral degree in computer science, electrical engineering, mathematics, or similar field preferred.
What category does the Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) role belong to?expand_more
Lead Machine Learning Engineer (MLOps, KServe building Kubernetes Clusters, PyTorch, TensorFlow on AWS) at Capital One is part of the it job category on Recrutus.