Machine Learning Engineer 4 – Machine Learning Foundations, MLX

Capital One

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

Posted 10/23/2026full-timeUnited StatesMid-LevelSenior💰 $179,400 - $245,600 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 large-scale distributed systems. Proficient in programming languages such as Python and Java, and experienced in applying best practices for continuous integration and deployment.

Highest-signal resume keywords

Machine Learning Model DevelopmentCloud-Based Architecture (AWS, GCP, Azure)Python ProgrammingContinuous Integration and Deployment (CI/CD)Large-Scale Distributed Systems (Spark, Ray)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnKubernetesData Pipeline Development

Soft Skills

CollaborationProblem-SolvingCommunication

Tools & Technologies

AWSGCPAzureKubernetesSparkRay

Industry Keywords

AgileResponsible AIExplainable AIHyperparameter TuningModel Validation

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyPandasPythonPyTorchRayScalaScikit-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
Inform ML infrastructure decisions using knowledge of modeling techniques, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Write and test application code, develop and validate ML models, and automate tests and deployment
Collaborate within a cross-functional Agile team to create and enhance big data and ML applications
Retrain, maintain, and monitor models in production
Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
Construct optimized data pipelines to feed 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, and Scikit-learn
At least 4 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI or Machine Learning data
At least 2 years of experience deploying and operating Machine Learning solutions in production
At least 2 years of experience operating production services in the cloud using AWS, GCP, or Azure
At least 2 years of experience using Kubernetes to manage large-scale containerized Machine Learning software systems
Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
3+ years of experience optimizing ML algorithms, configurations, and infrastructure
3+ years of experience following software development best practices including source control, testing, code reviews, and CI/CD
3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans
3+ years of experience with supervised, semi-supervised, unsupervised, and reinforcement learning; regression, classification, and clustering; RNNs, CNNs, LSTMs, and Transformers; loss functions, hyperparameters, regularization, and model evaluation
3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
1+ years of experience as a technical lead developing ML solutions
Authored or co-authored a paper on an ML technique, model, or proof of concept
Capital One will not sponsor a new applicant for employment authorization or provide immigration-related support for this position

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 with disabilities
Drug-free workplace