Senior Machine Learning Engineer – IC

Capital One

. Design, build, and deliver machine learning models and components solving real-world business problems .

Posted 9/21/2026full-timeMcLean • Virginia • United StatesSenior💰 $209,000 - $262,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying machine learning models and solutions, with a strong focus on cloud architectures, CI/CD practices, and large-scale distributed systems. Proficient in programming languages such as Python and Java, and experienced in collaborating within Agile teams to enhance ML applications.

Highest-signal resume keywords

Machine Learning ExperienceCloud Services (AWS, GCP, Azure)Programming (Python, Java, Scala)Large-Scale Distributed Systems (Spark, Ray)Kubernetes Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnSparkRay

Soft Skills

Communication

Tools & Technologies

AWSGCPAzureKubernetesCI/CD

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's or Doctoral Degree (Preferred)

Industry Keywords

Machine Learning ModelsData PipelinesExplainable AIResponsible AIAgile Teams

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyOpen SourcePandasPythonPyTorchRayScalaScikit-LearnSparkTensorflowC++Go

About the role

Key responsibilities & impact

Design, build, and deliver machine learning models and components solving real-world business problems
Collaborate with Product and Data Science teams
Build and scale multi-tenant platforms for large-scale ML model training and serving
Inform ML infrastructure decisions using modeling techniques and issues such as model choice, feature selection, hyperparameter tuning, dimensionality, bias/variance, and validation
Write and test application code, develop and validate ML models, and automate tests and deployment
Collaborate within cross-functional Agile teams to create and enhance big data and ML applications
Retrain, maintain, and monitor production models
Build or leverage cloud architectures, technologies, and platforms to deliver optimized ML models at scale
Construct optimized data pipelines feeding ML models
Apply CI/CD practices, test automation, and monitoring for ML model and application deployment
Manage code to reduce vulnerabilities and govern models from a risk perspective
Apply Responsible and Explainable AI best practices
Use programming languages including Python, Scala, or Java

Requirements

What you’ll need

Bachelor's degree or higher in Computer Science, Machine Learning, or a related quantitative field
At least 6 years of programming experience with Python, Java, Golang, or C++
At least 6 years of Machine Learning experience with PyTorch or TensorFlow and Pandas, NumPy, and Scikit-learn
At least 6 years of experience using and operating large-scale distributed systems such as Spark and Ray to prepare AI/ML data
At least 4 years of experience deploying and operating Machine Learning solutions in production
At least 4 years of experience operating production cloud services using AWS, GCP, or Azure
At least 4 years of experience using Kubernetes to manage large-scale containerized ML software systems
Ability to communicate complex technical and machine learning concepts clearly to varied audiences
Capital One will consider sponsoring a new qualified applicant for employment authorization
Preferred: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field
Preferred: 5+ years optimizing ML algorithms, configurations, and infrastructure
Preferred: 5+ years following software development best practices including source control, testing, code reviews, and CI/CD
Preferred: 5+ years building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response planning
Preferred: 5+ years working with ML techniques, model types, architectures, training concepts, and model evaluation
Preferred: 5+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
Preferred: ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents

Benefits

Comp & perks

Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI)
Comprehensive health, financial, and other benefits supporting total well-being
Employment authorization sponsorship may be considered for a new qualified applicant
Reasonable accommodations for applicants with disabilities