Senior Machine Learning Engineer, AI Foundations

Capital One

Machine Learning Engineer designing and delivering ML applications that solve real-world problems. Collaborating with Agile teams at Capital One on advanced LLMs and AI systems.

Posted 6/10/2026full-timeMcLean • New York, Virginia • 🇺🇸 United StatesSenior💰 $161,800 - $184,600 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaNode.jsOpen SourcePythonPyTorchScalaScikit-LearnSparkTensorflow

About the role

Key responsibilities & impact

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
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, including test automation and monitoring, to ensure successful deployment of ML models and application code
Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI

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
1+ years of experience with data gathering and preparation for ML models
2+ years of experience developing performant, resilient, and maintainable code
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
3+ years of experience with distributed file systems or multi-node database paradigms
Contributed to open source ML software
Authored/co-authored a paper on a ML technique, model, or proof of concept
3+ years of experience building production-ready data pipelines that feed ML models
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion

Benefits

Comp & perks

Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonScalaJavascikit-learnPyTorchDaskSparkTensorFlowdata pipelinesML modeling techniques

Soft Skills

collaborationproblem-solvingcommunicationAgile methodologyteamworkcritical thinkingadaptabilitycreativityattention to detailresponsible AI practices

Certifications

Bachelor’s Degree