Sr. Director, Machine Learning Engineering (Remote-Eligible)

Capital One is an industry leader in using machine learning to create real-time, personalized customer experiences. The Sr. Director of Machine Learning Engineering will lead a high-performing engineering organization responsible for the Personalization Platform, defining technical strategies and driving the evolution of MLOps practices to enhance AI capabilities.

Responsibilities:

• Lead and scale a high-performing engineering organization responsible for the Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across Capital One products and services
• Define the technical strategy, delivery roadmap, and operating model for a portfolio spanning recommendation systems, ranking, decisioning, GenAI infrastructure, MLOps, and low-latency application-serving systems
• Build, develop, and manage engineers and engineering leaders; set a high bar for hiring, performance, talent density, coaching, and succession planning across the organization
• Partner cross-functionally with Product, Data Science, Cloud Infrastructure, and Machine Learning Platform teams to align strategy, prioritize investments, and co-develop advanced recommendation systems and algorithms serving Capital One users
• Drive the design, buildout, and operation of robust ML infrastructure and pipelines supporting feature extraction, model training, testing, guardrails, evaluation, deployment, and both real-time and batch inference with strong reliability, scalability, and operational rigor
• Architect low-latency, event-driven systems for real-time personalization and decisioning based on streaming data, user behavior, and contextual signals
• Drive the evolution of MLOps practices through automated, metrics-backed deployment workflows, validation and testing systems, model lifecycle governance, and scalable observability
• Guide the adoption of state-of-the-art AI and LLM optimization techniques to improve scalability, cost, latency, throughput, and reliability of large-scale production AI systems
• Provide organizational technical and people leadership by influencing architecture, engineering standards, delivery excellence, incident management, and cross-team strategy while mentoring managers, tech leads, and senior engineers
• Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more
• Attract and retain top talent in the AI industry and nurture personal and professional development for your team. Foster a culture of learning and staying abreast of the state-of-the-art in AI

Requirements:

• Bachelor's degree in Computer Science, Engineering, or AI plus at least 10 years of experience developing or leading AI and ML algorithms or technologies, or Master's degree plus at least 8 years of experience developing or leading AI and ML algorithms or technologies
• At least 5 years of people leadership experience
• 7 years of experience managing and leading an engineering team
• 8+ years of experience deploying scalable, responsible AI solutions on major cloud platforms (AWS, GCP, Azure)
• Master's or PhD in Computer Science or a relevant technical field
• Proven expertise designing, implementing, and scaling personalization platforms and recommendation systems across feed personalization, ads ranking, or targeted marketing messaging
• Proficiency in Python, Java, C++, or Golang; hands-on experience with ML frameworks (PyTorch, TensorFlow) and orchestration tools (Databricks, Airflow, Kubeflow)
• Experience optimizing large-scale training and inference systems for hardware utilization, latency, throughput, and cost
• Deep expertise in cloud-native engineering, containerization (Docker, Kubernetes), and automated CI/CD deployment
• Deep experience with MLOps, model observability, and production ML lifecycle management
• Strong track record building organizations, developing managers and senior engineers, and leading through scale and ambiguity
• Excellent communication and presentation skills, with the ability to influence senior stakeholders and articulate complex AI concepts clearly
• Proven leadership in driving platform strategy, cross-functional execution, and technical direction across a large organization
• Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers