Machine Learning Engineer, Manager

Capital One

. Design, build, and deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams .

Posted 10/8/2026full-timeUnited StatesMid-LevelSenior💰 $197,300 - $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 ExperiencePython ProgrammingCloud Services (AWS, GCP, Azure)Kubernetes ManagementData Pipeline Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnSparkRay

Soft Skills

CollaborationProblem SolvingAgile Methodologies

Tools & Technologies

KubernetesCI/CDCloud-Based Architectures

Certifications & Qualifications

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

Industry Keywords

Machine Learning TechniquesModel EvaluationData GovernanceResponsible 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 in collaboration with Product and Data Science teams
Inform ML infrastructure decisions based on modeling techniques and issues, including model, data, and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
Collaborate on cross-functional Agile teams to create and enhance software enabling state-of-the-art 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 best practices, including test automation and monitoring
Ensure code is well-managed to reduce vulnerabilities and models are well-governed 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 (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
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 libraries including 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 and operating production services in the cloud (AWS, GCP, Azure)
At least 2 years of experience using Kubernetes to manage large-scale containerized Machine Learning software systems
No employer-sponsored immigration support or new employment authorization sponsorship available
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, model 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+ year as a technical lead developing ML solutions using industry best practices, patterns, and automation
Preferred: Authored or 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
Reasonable accommodations for applicants who require them