Machine Learning Engineer – Intelligent Foundations and Experiences

Capital One

Machine Learning Engineer at Capital One focusing on productionizing ML applications and systems at scale. Collaborating in an Agile environment to solve complex business problems using ML.

Posted 5/7/2026full-timeRichmond • Texas, Virginia • 🇺🇸 United StatesMid-LevelSenior💰 $147,100 - $167,900 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaNode.jsOpen SourcePythonPyTorchScalaScikit-LearnSparkTensorflow

About the role

Key responsibilities & impact

Participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms.
Design, build, and/or deliver ML models and components that solve real-world business problems, while collaborating with the Product and Data Science teams.
Inform ML infrastructure decisions using understanding of ML modeling techniques and issues.
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
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.
Retrain, maintain, and monitor models in production.
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Leverage continuous integration and continuous deployment best practices to ensure successful deployment of ML models and application code.

Requirements

What you’ll need

Bachelor’s Degree
At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply)
At least 3 years of experience designing and building data-intensive solutions using distributed computing
At least 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)
At least 1 year of experience productionizing, monitoring, and maintaining models
1+ years of experience building, scaling, and optimizing ML systems (preferred)
1+ years of experience with data gathering and preparation for ML models (preferred)
2+ years of experience developing performant, resilient, and maintainable code (preferred)
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform (preferred)
Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field (preferred)
3+ years of experience with distributed file systems or multi-node database paradigms (preferred)
Contributed to open source ML software (preferred)
Authored/co-authored a paper on a ML technique, model, or proof of concept (preferred)
3+ years of experience building production-ready data pipelines that feed ML models (preferred)
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance (preferred).

Benefits

Comp & perks

Comprehensive, competitive, and inclusive benefits that support total well-being.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonScalaJavamachine learningML frameworksscikit-learnPyTorchDaskSparkTensorFlow

Soft Skills

collaborationproblem-solvingcommunicationcross-functional teamworkagile methodology

Certifications

Bachelor's DegreeMaster's DegreeDoctoral Degree