Capital One
Lead Machine Learning Engineer (MLOps, KServe + building Kubernetes Clusters, PyTorch, TensorFlow on AWS)
McLean, Virginia · Part-time
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Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
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This range already reaches Level IV — the maximum four lottery entries.
DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · Washington-Arlington-Alexandria, DC-VA-MD-WV. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
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About the role
- Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).
- Construct optimized data pipelines to feed ML models.
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
Responsibilities
- Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.
- Retrain, maintain, and monitor models in production.
Requirements
- Bachelor's degree
- At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
- At least 4 years of experience programming with Python, Scala, or Java
- At least 2 years of experience building, scaling, and optimizing ML systems
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 designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
Skills
- Use programming languages like Python, Scala, or Java.
Compensation
- $197k-$225k
Benefits
- The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life.
- As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale.
- You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms.
- You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications.
- You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.
Company info
- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
Visa & Work Authorization
- At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i
This listing is sourced directly from Capital One's careers page and normalized into a canonical job model.