iHire
Senior Machine Learning Engineer (Python, AWS, Big Data) – Auto Loan Valuations & Insights
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You will be responsible for detailed technical design, development, and implementation of machine learning solutions using existing and emerging platforms. Your role focuses on machine learning architectural design, reviewing model and application code, and ensuring high availability and performance of our ML applications. This position offers continuous learning and application of the latest innovations and best practices.
What You’ll Do In The Role
The MLE role overlaps with many disciplines, including Ops, Modeling, and Data Engineering. You will perform many ML engineering activities, such as designing, building, and delivering ML models and components that solve real‑world business problems in collaboration with Product and Data Science.
Inform infrastructure decisions with a deep understanding of modeling techniques, data and feature selection, training, hyperparameter tuning, dimensionality reduction, bias/variance, and validation.
Write and test application code, develop and validate models, and automate tests and deployment.
Collaborate on a cross‑functional Agile team to create and enhance software that powers big data and ML applications.
Retrain, maintain, and monitor models in production.
Leverage or build cloud‑based architectures, technologies, or platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply CI/CD best practices, including test automation and monitoring, to ensure successful deployment.
Ensure code and models are well‑managed, safely governed, and comply with Responsible and Explainable AI principles.
Code in Python, Scala, or Java.
Basic Qualifications
Bachelor’s degree
At least 4 years of programming experience with Python, Scala, or Java (internship experience not counted)
At least 3 years designing and building data‑intensive solutions using distributed computing
At least 2 years with an industry‑recognized ML framework such as scikit‑learn, PyTorch, Dask, Spark, or TensorFlow
At least 1 year producing, monitoring, and maintaining models in production
Preferred Qualifications
1+ years building, scaling, and optimizing ML systems
1+ years of data gathering and preparation for ML models
2+ years developing performant, resilient, and maintainable code
Experience deploying ML solutions in a public cloud (AWS, Azure, or GCP)
Master’s or doctoral degree in computer science, electrical engineering, mathematics, or related field
3+ years with distributed file systems or multi‑node database paradigms
Contributions to open‑source ML software
Authored or co‑authored a paper on an ML technique or model
3+ years building production‑ready data pipelines that feed ML models
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating performance
At this time, Capital One will not sponsor a new applicant for employment authorization or offer any immigration‑related support for this position.
Salary: McLean, VA – $158,600 – $181,000; Plano, TX – $144,200 – $164,600 for Senior Machine Learning Engineer. Additional locations will follow local pay ranges.
Compensation also includes performance‑based incentive compensation and long‑term incentives per Capital One policy.
Capital One is an equal‑opportunity employer (EOE) committed to non‑discrimination. We accept qualified applicants with a criminal history in compliance with applicable laws. For accommodations, please contact Capital One Recruiting.
References to supporting materials and policies are available on the Capital One Careers website.
#J-18808-Ljbffr
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You will be responsible for detailed technical design, development, and implementation of machine learning solutions using existing and emerging platforms. Your role focuses on machine learning architectural design, reviewing model and application code, and ensuring high availability and performance of our ML applications. This position offers continuous learning and application of the latest innovations and best practices.
What You’ll Do In The Role
The MLE role overlaps with many disciplines, including Ops, Modeling, and Data Engineering. You will perform many ML engineering activities, such as designing, building, and delivering ML models and components that solve real‑world business problems in collaboration with Product and Data Science.
Inform infrastructure decisions with a deep understanding of modeling techniques, data and feature selection, training, hyperparameter tuning, dimensionality reduction, bias/variance, and validation.
Write and test application code, develop and validate models, and automate tests and deployment.
Collaborate on a cross‑functional Agile team to create and enhance software that powers big data and ML applications.
Retrain, maintain, and monitor models in production.
Leverage or build cloud‑based architectures, technologies, or platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply CI/CD best practices, including test automation and monitoring, to ensure successful deployment.
Ensure code and models are well‑managed, safely governed, and comply with Responsible and Explainable AI principles.
Code in Python, Scala, or Java.
Basic Qualifications
Bachelor’s degree
At least 4 years of programming experience with Python, Scala, or Java (internship experience not counted)
At least 3 years designing and building data‑intensive solutions using distributed computing
At least 2 years with an industry‑recognized ML framework such as scikit‑learn, PyTorch, Dask, Spark, or TensorFlow
At least 1 year producing, monitoring, and maintaining models in production
Preferred Qualifications
1+ years building, scaling, and optimizing ML systems
1+ years of data gathering and preparation for ML models
2+ years developing performant, resilient, and maintainable code
Experience deploying ML solutions in a public cloud (AWS, Azure, or GCP)
Master’s or doctoral degree in computer science, electrical engineering, mathematics, or related field
3+ years with distributed file systems or multi‑node database paradigms
Contributions to open‑source ML software
Authored or co‑authored a paper on an ML technique or model
3+ years building production‑ready data pipelines that feed ML models
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating performance
At this time, Capital One will not sponsor a new applicant for employment authorization or offer any immigration‑related support for this position.
Salary: McLean, VA – $158,600 – $181,000; Plano, TX – $144,200 – $164,600 for Senior Machine Learning Engineer. Additional locations will follow local pay ranges.
Compensation also includes performance‑based incentive compensation and long‑term incentives per Capital One policy.
Capital One is an equal‑opportunity employer (EOE) committed to non‑discrimination. We accept qualified applicants with a criminal history in compliance with applicable laws. For accommodations, please contact Capital One Recruiting.
References to supporting materials and policies are available on the Capital One Careers website.
#J-18808-Ljbffr