Senior Machine Learning Engineer 5

Capital One

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

Posted 10/28/2026full-timeUnited StatesSenior💰 $229,900 - $286,200 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 CI/CD practices and optimizing ML algorithms.

Highest-signal resume keywords

Machine Learning ExperiencePython ProgrammingCloud Services (AWS, GCP, Azure)Kubernetes ManagementLarge-Scale Distributed Systems (Spark, Ray)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnCI/CDData Pipeline Development

Soft Skills

Communication of Technical Concepts

Tools & Technologies

AWSGCPAzureKubernetesSparkRay

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's or Doctoral Degree in Related Field

Industry Keywords

Machine Learning TechniquesModel EvaluationResponsible AIExplainable AIAgile Methodologies

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyOpen SourcePandasPythonPyTorchRayScalaScikit-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
Build and scale multi-tenant platforms for large-scale ML model training and serving
Inform ML infrastructure decisions using knowledge of modeling techniques, data, feature selection, training, tuning, dimensionality, bias/variance, and validation
Write and test application code, develop and validate ML models, and automate testing and deployment
Collaborate in 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 for ML models
Apply CI/CD, test automation, and monitoring practices for model and application deployment
Manage code to reduce vulnerabilities, govern models from a risk perspective, and 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 6 years of programming experience with Python, Java, Golang, or C++
At least 6 years of Machine Learning experience using PyTorch or TensorFlow and Pandas, NumPy, or Scikit-learn
At least 6 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI or Machine Learning 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 Machine Learning software systems
Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field (preferred)
5+ years of experience optimizing ML algorithms, configurations, and infrastructure (preferred)
5+ years of experience following software development best practices including source control, testing, code reviews, and CI/CD (preferred)
5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans (preferred)
5+ years of experience with Machine Learning techniques, model types, architectures, training concepts, and model evaluation (preferred)
5+ years of experience 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 (preferred)
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

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
Reasonable accommodations for applicants who require them