Machine Learning Engineer

Capital One

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

Posted 9/22/2026full-timeUnited StatesMid-LevelSenior💰 $179,400 - $225,100 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-based architectures and big data applications. Proficient in programming languages such as Python and Java, and experienced in utilizing frameworks like PyTorch and TensorFlow for model development and optimization.

Highest-signal resume keywords

Machine Learning ExperiencePython ProgrammingCloud-Based ArchitecturesKubernetes ManagementContinuous Integration/Continuous Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnSparkRay

Soft Skills

CollaborationProblem-SolvingCommunicationAgile Methodologies

Tools & Technologies

AWSGCPAzureKubernetesCI/CD

Certifications & Qualifications

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

Industry Keywords

Machine Learning TechniquesData PipelinesModel EvaluationResponsible AIExplainable AI

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyPandasPythonPyTorchRayScalaScikit-LearnSparkTensorflowC++Go

About the role

Key responsibilities & impact

Design, build, and deliver ML models and components solving real-world business problems
Collaborate with Product and Data Science teams
Inform ML infrastructure decisions using modeling techniques and issues, including model, data, feature selection, training, tuning, dimensionality, bias/variance, and validation
Write and test application code, develop and validate ML models, and automate tests and deployment
Collaborate on cross-functional Agile teams to create and enhance big data and ML applications
Retrain, maintain, and monitor production models
Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
Construct optimized data pipelines feeding ML models
Apply continuous integration and continuous deployment practices, including test automation and monitoring
Manage code to reduce vulnerabilities and govern models from a risk perspective
Apply Responsible and Explainable AI best practices
Use programming languages such as 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 4 years of experience programming with Python, Java, Golang, or C++
At least 4 years of Machine Learning experience using PyTorch or Tensorflow and Pandas, NumPy, Scikit-learn
At least 4 years of experience using and operating large scale distributed systems such as Spark or Ray to prepare AI/ML data
At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in AWS, GCP, or Azure
At least 2 years of experience using Kubernetes to manage large scale containerized ML software systems
No employer sponsorship or immigration-related support for new applicants
Preferred: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
Preferred: 3+ years optimizing ML algorithms, configurations, and infrastructure
Preferred: 3+ years following software development best practices, including source control, testing, code reviews, and CI/CD
Preferred: 3+ years building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans
Preferred: 3+ years working with Machine Learning techniques, model types, architectures, training concepts, and model evaluation
Preferred: 3+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
Preferred: 1+ years as a technical lead developing ML solutions
Preferred: Authored/co-authored a paper on an ML technique, model, or proof of concept

Benefits

Comp & perks

Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Comprehensive, competitive, and inclusive health, financial and other benefits supporting total well-being
Equal opportunity employer committed to non-discrimination
Reasonable accommodations for applicants who require them