Capital One
Senior Machine Learning Engineer (Intelligent Foundations & Experiences)
Capital One, Mc Lean, Virginia, us, 22107
Senior Machine Learning Engineer (Intelligent Foundations & Experiences)
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Senior Machine Learning Engineer (Intelligent Foundations & Experiences)
role at
Capital One . 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’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.
Intelligent Foundations & Experiences (IFX)
is a powerful collective of horizontal technology organizations that are driving Capital One’s real‑time intelligent future. Together with our partners in the Enterprise and across lines of business, we deliver broad‑reaching technical solutions and advance state‑of‑the‑art science to help every Capital One associate and our 100+M customers succeed.
What you’ll do in the role
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
Design, build, and/or deliver ML models and components that solve real‑world business problems, while working in collaboration with the Product and Data Science teams.
Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model 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 as part of a cross‑functional Agile team to create and enhance software that enables state‑of‑the‑art big data and ML applications.
Retrain, maintain, and monitor models in production.
Leverage or build cloud‑based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
Ensure all code is well‑managed to reduce vulnerabilities, models are well‑governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
Use programming languages like Python, Scala, or Java.
Basic Qualifications
Bachelor’s degree
At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply)
At least 3 years of experience designing and building data‑intensive solutions using distributed computing
At least 2 years of on‑the‑job experience with an industry‑recognized ML frameworks (scikit‑learn, PyTorch, Dask, Spark, or TensorFlow)
At least 1 year of experience productionizing, monitoring, and maintaining models
Preferred Qualifications
1+ years of experience building, scaling, and optimizing ML systems
1+ years of experience with data gathering and preparation for ML models
2+ years of experience developing performant, resilient, and maintainable code
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
3+ years of experience with distributed file systems or multi‑node database paradigms
Contributed to open source ML software
Authored/co‑authored a paper on a ML technique, model, or proof of concept
3+ years of experience building production‑ready data pipelines that feed ML models
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F‑1 OPT, F‑1 STEM OPT, F‑1 CPT, J‑1, TN, E‑2, E‑3, L‑1 and O‑1, or any EADs or other forms of work authorization that require immigration support from an employer).
Salary range: McLean, VA: $158,600 - $181,000 New York, NY: $173,000 - $197,400 Richmond, VA: $144,200 - $164,600 The role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long‑term incentives (LTI). Incentives could be discretionary or non‑discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well‑being. Learn more at the Capital One Careers website. Eligibility varies based on full or part‑time status, exempt or non‑exempt status, and management level.
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23‑A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901‑4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com. Capital One does not provide, endorse nor guarantee and is not liable for third‑party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
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Senior Machine Learning Engineer (Intelligent Foundations & Experiences)
role at
Capital One . 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’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.
Intelligent Foundations & Experiences (IFX)
is a powerful collective of horizontal technology organizations that are driving Capital One’s real‑time intelligent future. Together with our partners in the Enterprise and across lines of business, we deliver broad‑reaching technical solutions and advance state‑of‑the‑art science to help every Capital One associate and our 100+M customers succeed.
What you’ll do in the role
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
Design, build, and/or deliver ML models and components that solve real‑world business problems, while working in collaboration with the Product and Data Science teams.
Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model 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 as part of a cross‑functional Agile team to create and enhance software that enables state‑of‑the‑art big data and ML applications.
Retrain, maintain, and monitor models in production.
Leverage or build cloud‑based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
Ensure all code is well‑managed to reduce vulnerabilities, models are well‑governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
Use programming languages like Python, Scala, or Java.
Basic Qualifications
Bachelor’s degree
At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply)
At least 3 years of experience designing and building data‑intensive solutions using distributed computing
At least 2 years of on‑the‑job experience with an industry‑recognized ML frameworks (scikit‑learn, PyTorch, Dask, Spark, or TensorFlow)
At least 1 year of experience productionizing, monitoring, and maintaining models
Preferred Qualifications
1+ years of experience building, scaling, and optimizing ML systems
1+ years of experience with data gathering and preparation for ML models
2+ years of experience developing performant, resilient, and maintainable code
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
3+ years of experience with distributed file systems or multi‑node database paradigms
Contributed to open source ML software
Authored/co‑authored a paper on a ML technique, model, or proof of concept
3+ years of experience building production‑ready data pipelines that feed ML models
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F‑1 OPT, F‑1 STEM OPT, F‑1 CPT, J‑1, TN, E‑2, E‑3, L‑1 and O‑1, or any EADs or other forms of work authorization that require immigration support from an employer).
Salary range: McLean, VA: $158,600 - $181,000 New York, NY: $173,000 - $197,400 Richmond, VA: $144,200 - $164,600 The role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long‑term incentives (LTI). Incentives could be discretionary or non‑discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well‑being. Learn more at the Capital One Careers website. Eligibility varies based on full or part‑time status, exempt or non‑exempt status, and management level.
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23‑A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901‑4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com. Capital One does not provide, endorse nor guarantee and is not liable for third‑party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
#J-18808-Ljbffr