Ever since our first credit card customer in 1994, Capital One has recognized that technology and data can enable even large companies to be innovative and personalized. As one of the first large enterprises to go all-in on the public cloud, Capital One needed to build cloud and data management tools that didn’t exist in the marketplace to enable us to operate at scale in the cloud. And in 2022, we publicly announced Capital One Software and brought our first B2B software solution, Slingshot, to market.
Building on Capital One’s pioneering adoption of modern cloud and data capabilities, Capital One Software is helping accelerate the data management journey at scale for businesses operating in the cloud. If you think of the kind of challenges that companies face – things like data publishing, data consumption, data governance, and infrastructure management – we’ve built tools to address these various needs along the way. Capital One Software will continue to explore where we can bring our solutions to market to help other businesses address these same needs going forward.
We are seeking top tier talent to join our pioneering team and propel us towards our destination. You will be joining a team of innovative product, tech, and design leaders that tirelessly seek to question the status quo. As a Capital One Lead Machine Learning Engineer, you’ll have the opportunity to be on the forefront of building this business and bring these tools to market.
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 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
Preferred Qualifications:
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
3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
2+ years of experience developing performant, resilient, and maintainable code
2+ years of experience with data gathering and preparation for ML models
2+ years of people leader experience
1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
At this time, Capital One will not sponsor a new applicant for employment authorization for this position.
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.
Remote (Regardless of Location): $170,700 - $194,800 for Lead Machine Learning EngineerCandidates 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.
This 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.
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
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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).