Capital One

Machine Learning Engineer

• Design, build, and deliver ML models and components solving real-world business problems
• Collaborate with Product and Data Science teams
• Inform ML infrastructure decisions using modeling techniques and issues, including model, data, feature selection, training, tuning, dimensionality, bias/variance, and validation
• Write and test application code, develop and validate ML models, and automate tests and deployment
• Collaborate on cross-functional Agile teams to create and enhance big data and ML applications
• Retrain, maintain, and monitor production models
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines feeding ML models
• Apply continuous integration and continuous deployment practices, including test automation and monitoring
• Manage code to reduce vulnerabilities and govern models from a risk perspective
• Apply Responsible and Explainable AI best practices
• Use programming languages such as Python, Scala, or Java

Data Engineer

• Collaborate across Agile teams to design, develop, test, implement, and support technical solutions
• Influence developers, data analysts, and data scientists working with machine learning, distributed microservices, lakehouse architecture, and full-stack systems
• Use Python and Spark, open-source relational and NoSQL databases, and cloud data warehousing platforms including Databricks and Snowflake
• Stay current with data trends, experiment with new technologies, participate in technology communities, and mentor data professionals
• Collaborate with product managers and software engineers to deliver cloud-first data solutions
• Independently design, build, and deliver cloud data solutions and applications
• Architect and enforce common data engineering design patterns across platforms and pipelines
• Communicate technical concepts and data outcomes to internal and external stakeholders
• Design and build scalable, resilient, and operationally efficient data pipelines and platforms
• Mentor peers and junior engineers while contributing hands-on technical expertise and innovation
• Lead large-scale data initiatives end to end, making architectural decisions and evaluating platform choices such as Snowflake versus Databricks

Data Engineer 5 – Enterprise Platforms Technology

• Design, build, and deliver machine learning models and components solving real-world business problems
• Collaborate with Product and Data Science teams
• Inform ML infrastructure decisions regarding models, data, feature selection, training, tuning, dimensionality, bias/variance, and validation
• Write and test application code, develop and validate ML models, and automate testing and deployment
• Collaborate in cross-functional Agile teams to create and enhance big data and ML applications
• Retrain, maintain, and monitor production models
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines feeding ML models
• Apply continuous integration and continuous deployment practices, including test automation and monitoring
• Ensure code is well-managed to reduce vulnerabilities, models are governed from a risk perspective, and ML follows Responsible and Explainable AI best practices
• Use programming languages including Python, Scala, or Java

Product Development

• Participate in a two-year rotational program with two distinct roles and rotations at the one-year mark
• Gain hands-on experience leveraging new technology while building product management skills
• Develop leadership skills through interaction with product leaders
• Use data and customer insights to understand problems, make strategic decisions, and lead technology teams
• Participate in experiential education weeks focused on leadership development, strategic problem-solving, and networking
• Partner with product managers to ship, scale, and improve products
• Influence product direction, prioritize features, manage the backlog, and remove team impediments
• Own feature success and product/platform lifecycle, strategy, and prioritization
• Use business strategy, market analysis, user feedback, analytics, and technology best practices to test and learn
• Partner with experts to develop and drive long-term business strategy
• Collaborate with internal partners, governance and risk forums, and diverse stakeholders
• Engage developers, designers, analysts, data scientists, customers, and subject-matter experts to build products
• Potentially remain in the final role placement or explore other Capital One opportunities after program completion

Product Development Program Associate

Operations

• Provide technical support through remote access tools to resolve internal end-user issues and achieve first-call resolution
• Monitor and respond quickly and effectively to tickets, calls, chats, or in-person service requests
• Interact with end users via soft phone or chat modules to resolve software and hardware issues
• Utilize ServiceNow as the ticketing tool to track and escalate tickets for end users
• Assist with onboarding new Agents by training them and allowing others to shadow
• Perform other tasks as needed and instructed by the manager

Principal Coordinator, Tech Operations

Product Manager

• Deliver products supporting the strategy and operations of a Top 10 Commercial Bank
• Craft strategies for tailored client solutions, financial advisors, and origination and management of loans, treasury, and capital markets products
• Build “always on” technologies to increase profitability and empower associates
• Reduce operational risks by strengthening controls and monitoring key processes
• Own the strategy and delivery of new products and features in partnership with technology teams
• Collaborate with Product, Tech, and Design partners to deliver shared outcomes
• Reimagine and innovate product solutions around internal and external customer needs
• Deliver business outcomes through leverage and execution excellence
• Leverage technology for innovative and resilient solutions
• Identify and resolve complex problems while mitigating product risks
• Lead cross-functional teams and drive organizational alignment

Senior Associate, Product Management – Commercial Bank

Full-stack Engineer

• Design and scale resilient, low-latency global payment architectures for high-volume, cross-border transaction processing and multi-currency financial workflows
• Leverage AI-powered coding assistants and agentic workflows to accelerate delivery, automate routine tasks, and write secure, robust code
• Lead diverse technology projects and a team of developers experienced in distributed microservices and full-stack systems
• Share technology knowledge, experiment with emerging technologies, participate in technology communities, and mentor engineers
• Collaborate with digital product managers to deliver robust cloud-based solutions supporting financial empowerment
• Use Java, Scala, Python, Go, Node.js, JavaScript, TypeScript, open-source RDBMS and NoSQL databases, Docker, Kubernetes, and AWS services
• Provide technical leadership across multiple teams and shape system architecture and long-term technology direction
• Drive end-to-end design and implementation of large-scale distributed and cloud-native systems
• Partner with architects and product leaders to define platform roadmaps and shared-services strategy

Full-Stack Engineer 5, Java, AWS, Kafka

• Lead a portfolio of diverse technology projects and a team of developers with experience in distributed microservices and full-stack systems
• Create solutions that help meet the company's regulatory needs
• Stay current with technology trends and experiment with and learn new technologies
• Participate in internal and external technology communities
• Mentor members of the engineering community
• Collaborate with digital product managers to deliver robust cloud-based solutions
• Use programming languages, databases, container orchestration services, and AWS tools and services
• Provide technical leadership and direction across Agile teams
• Set standards for design, development, and operational excellence
• Partner with architects to define service boundaries, integration patterns, and reusable frameworks
• Identify areas for improvement, implement targeted refactoring, reduce technical debt, and enhance system performance
• Influence long-term technology strategy through evaluation of emerging tools and practices in a “you build it, you own it” environment

• Collaborate with Agile teams to design, develop, test, implement, and support technical solutions
• Influence developers, data analysts, and data scientists across machine learning, distributed microservices, lakehouse architecture, and full-stack systems
• Use Python and Spark, open-source relational and NoSQL databases, and cloud-based data warehousing platforms including Databricks and Snowflake
• Stay current with data trends, experiment with new technologies, participate in technology communities, and mentor data community members
• Collaborate with product managers and software engineers to deliver cloud-first data solutions
• Independently design, build, and deliver cloud data solutions and applications
• Architect and enforce data engineering design patterns across platforms and pipelines
• Communicate technical concepts and data outcomes to internal and external stakeholders
• Design and build scalable, resilient, and operationally efficient data pipelines and platforms
• Implement data security standards, including encryption and fine-grained access control, to support privacy compliance

Loan Specialist

• Apply job knowledge and documentation expertise to complex lending products
• Prepare commercial loan documents using LaserPro, Etran, and Word templates for new loans, renewals, extensions, and modifications
• Conduct lien searches and related due diligence; resolve outstanding lien issues and deliver lien documents to vendors
• Review loan documents for compliance with Bank policy, banking regulations, government-related SOPs, and credit approval requirements; authorize loan closings
• Review organizational documents, appraisals, surveys, flood determinations, flood policies, and other third-party documents
• Analyze due diligence documentation and prepare lien perfection documents to protect the bank’s security interest
• Identify and clear exceptions within required time periods
• Coordinate issuance, renewals, and amendments of letters of credit
• Coordinate with underwriters, relationship managers, customers, agent banks, attorneys, title companies, and insurance agents to meet closing conditions
• Instruct new associates and train less experienced associates
• Manage complex workloads involving multiple tasks and conflicting priorities
• Remain current on regulatory changes and attend compliance seminars and workshops
• Maintain expert knowledge of lending processes, loan principles, banking laws, regulations, disclosures, and requirements

Senior Associate, Loan Documentation – Closing Specialist

AI Engineer

• Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products
• Design, develop, test, deploy, and support AI software components including foundation model training, LLM inference, agents, multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability
• Leverage open-source and SaaS AI technologies including AWS Ultraclusters, Hugging Face, VectorDBs, and PyTorch
• Invent and introduce foundation model optimization techniques to improve scalability, cost, latency, and throughput
• Contribute to the technical vision and long-term roadmap of foundational AI systems
• Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models
• Establish and lead cost-performance governance reviews, tracking GPU utilization, model throughput, and inference cost efficiency
• Lead team design councils and design review boards to ensure technical consistency and compliance with AI engineering standards
• Mentor Principal- and Manager-level AI engineers and elevate organizational technical maturity

AI Engineer 5 – AI Foundations, LLM Core, Agentic AI

Researcher

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products
• Use PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and other technologies to analyze large volumes of numeric and textual data
• Build AI foundation models through design, training, evaluation, validation, and implementation
• Conduct applied research to advance AI capabilities and customer experiences
• Translate complex research into tangible business goals
• Collaborate with senior researchers on internal technical reports and conference submissions

Applied Researcher, Level 4

• Design, build, and/or deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams
• Inform ML infrastructure decisions using knowledge of modeling techniques, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
• Write and test application code, develop and validate ML models, and automate tests and deployment
• Collaborate within a cross-functional Agile team to create and enhance software enabling big data and ML applications
• Retrain, maintain, and monitor models in production
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to feed ML models
• Apply continuous integration and continuous deployment practices, including test automation and monitoring
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow Responsible and Explainable AI best practices
• Use programming languages such as Python, Scala, or Java
• Support Marketing and Messaging platforms delivering hyper-personalized omnichannel messages and experiences

Machine Learning Engineer 4 – Python, AWS, SQL, GenAI

Project Manager

• Support and track projects from market identification and site selection through project intake, construction execution, and Customer Day 1
• Partner with cross-functional teams to build, refine, and present executive presentation decks and business cases for deal approval
• Establish, update, and manage master project schedules, calendars, and pipeline tracking tools
• Lead routine project check-ins, capture actionable meeting minutes, maintain risk/issue logs, and hold partners accountable to milestones
• Serve as an operational link between Market Strategy, Retail Design, Construction, Finance, and Legal teams
• Partner with data and analytics teams to integrate productivity and AI-driven automation tools into workflows
• Travel to target markets a few times per year for site visits and market planning sessions

Principal Associate, Project Manager – Market Planning

• Design, build, and/or deliver ML models and components solving real-world business problems
• Collaborate with Product and Data Science teams
• Inform ML infrastructure decisions using knowledge of modeling techniques and issues
• Write and test application code, develop and validate ML models, and automate tests and deployment
• Collaborate in a cross-functional Agile team to create and enhance software for big data and ML applications
• Retrain, maintain, and monitor models in production
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to feed ML models
• Apply continuous integration and continuous deployment best practices, including test automation and monitoring
• Ensure code is well-managed, models are governed from a risk perspective, and ML follows Responsible and Explainable AI best practices
• Use programming languages such as Python, Scala, or Java

Machine Learning Engineer

Data Engineer 5 – Enterprise Platforms Technology

Machine Learning Engineer

Product Development Program Associate

Principal Coordinator, Tech Operations

Senior Associate, Product Management – Commercial Bank

Full-Stack Engineer 5, Java, AWS, Kafka

Full-stack Engineer

Data Engineer

Senior Associate, Loan Documentation – Closing Specialist

AI Engineer 5 – AI Foundations, LLM Core, Agentic AI

Applied Researcher, Level 4

Machine Learning Engineer 4 – Python, AWS, SQL, GenAI

Principal Associate, Project Manager – Market Planning

Machine Learning Engineer 4, Python, AWS, SQL, GenAI