Capital One
Financial Services
LeadMachineLearningEngineer
Neural analysis suggests this role is
optimal for Lead candidates.
“Lead Machine Learning Engineer at Capital One. Skills: Machine Learning Engineering, Productionizing ML applications at scale, ML architectural design, Data pipelines for ML models, Cloud-based ML solutions. Productionizing machine learning applications and systems at scale. Detailed technical design, development, and implementation of machine learning applications”
What You'll Achieve.
Deliver 'platinum grade' customer experiences; Make a broad impact across the company
Industry & Context.
Solve complex problems by writing and testing application code; Solve complex, large-scale data and ML challenges
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).
What They're Looking For.
Must Have
Bachelor's Degree, At least 6 years of experience designing and building data-intensive solutions using distributed computing, At least 4 years of experience programming with Python, Scala, or Java, At least 2 years of experience building, scaling, and optimizing ML systems
Nice to Have
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, Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion
What You'll Do.
Productionizing machine learning applications and systems at scale
Detailed technical design
and implementation of machine learning applications
Focus on machine learning architectural design
Develop and review model and application code
Ensure high availability and performance of machine learning applications
and/or deliver ML models and components that solve real-world business problems
Inform ML infrastructure decisions
Solve complex problems by writing and testing application code
Develop and validate ML models
Automate tests and deployment
and monitor models in production
Leverage or build cloud-based architectures
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
Ensure all code is well-managed to reduce vulnerabilities
Ensure models are well-governed from a risk perspective
Follow best practices in Responsible and Explainable AI
How You'll Work.
Team & Collaboration
Participate in Agile team; Work in collaboration with the Product and Data Science teams; Collaborate as part of a cross-functional Agile team
Full Job Description
Lead Machine Learning Engineer 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. **About the Team:** The Transaction Core team, a key part of the PINT (Payments Intelligence) organization, is dedicated to building and maintaining the foundational data platforms that empower Capital One to understand and act on customer spend. Our mission is to provide an actionable understanding of purchase transactions to enrich our customer's financial lives through real-time, intelligent, and resilient platform-based services. We manage the core services like the Transaction Datastore (TDS), which processes billions of transactions annually and serves as the 'transaction core' for numerous machine learning models, including those for fraud and subscription detection. We are currently focused on completing the Transaction Core to include a universal view of all payment types—Card, Bank (Debit & ACH), and External FI transactions. This modernization effort involves componentizing our data assets for technical flexibility and improving the velocity of our recurring insights models. Our work is critical, powering engaging digital experiences like subscription management and enhanced transaction views in customer-facing applications (EASE) and agent tools (Empath). If you are passionate about solving complex, large-scale data and ML challenges to deliver 'platinum grade' customer experiences, th
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