Lead Machine Learning Engineer (Python, GoLang, AWS)

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.

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

At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)

At least 4 years of experience programming with Python, Scala, or Java

At least 2 years of experience building, scaling, and optimizing ML systems

Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field

3+ years of experience building production-ready data pipelines that feed ML models

The minimum and maximum full-time annual salaries for this role are listed below, by location.

Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting.

Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.

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.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com.

All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

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).