About the Role
At Capital One, we are leveraging machine learning and generative AI to deliver real-time, personalized financial experiences at scale. As a Sr. Distinguished Machine Learning Engineer within the Consumer Engagement Platform organization, you will lead the architecture, engineering strategy, and platform development for our Hyper-Personalization infrastructure. You will drive high-impact ML initiatives, turning every customer channel—from mobile home feeds to targeted messaging—into a context-aware decisioning engine.
Key Responsibilities
Technical Leadership & Strategy: Define and execute the multi-year platform roadmap for multi-channel personalization and recommendation systems across all Capital One products.
Architecture & Scalability: Architect low-latency, event-driven streaming systems capable of real-time dynamic decisioning and personalization using continuous behavioral signals.
ML Pipelines & MLOps: Design and maintain scalable ML infrastructure for feature extraction, model training, deployment, and monitoring. Build automated, metrics-backed CI/CD and MLOps deployment workflows.
Rules & Decisioning Engines: Co-develop flexible, dynamic rules engines to combine business targeting logic with advanced ML/AI-driven recommendation systems.
GenAI & LLM Optimization: Implement state-of-the-art LLM optimization techniques to optimize hardware utilization, lower throughput costs, and reduce latency for large-scale production AI systems.
Cross-Functional Collaboration: Partner with Product, Data Science, Cloud Infrastructure, and Platform engineering teams to align strategies and set company-wide technical standards.
Qualifications
Basic Qualifications
Education: Bachelor’s degree in Computer Science, Applied Mathematics, or a related technical field.
Experience:
10+ years designing and building data-intensive systems using distributed computing.
7+ years of hands-on programming experience in Python, C, C++, or Scala.
4+ years leading the full ML development lifecycle in a business-critical production setting.
Preferred Qualifications
Advanced Degree: Master’s degree or Ph.D. in Computer Science or a technical domain.
AI & Recommendation Platforms: 5+ years experience building and scaling large-scale personalization, feed ranking, ads recommendation, or targeted marketing systems.
Frameworks & Orchestration: Deep experience with PyTorch, TensorFlow, Databricks, Airflow, Kubeflow, Docker, and Kubernetes.
Cloud & Modern AI Stack: 8+ years deploying scalable AI solutions on major cloud platforms (AWS, GCP, Azure). Hands-on familiarity with tools such as AWS Ultraclusters, VectorDBs, NeMo Guardrails, and Hugging Face.
Inference Optimization: Proven track record in optimizing training/inference pipelines for hardware efficiency, latency, throughput, and cost reduction.
What We Offer
Competitive Compensation: Annual base salary plus performance-based incentive compensation (cash bonuses and/or long-term incentives).
Comprehensive Benefits: Inclusive health, wellness, and financial benefits package supporting total well-being.
Workplace Flexibility: Remote-eligible options with flexible work policies.
Sponsorship: Capital One will consider sponsoring qualified applicants for employment authorization for this position