Senior Director, Machine Learning Engineering

🕒 April 8

⚔️ Virginia – Remote

⚔️ Virginia – Remote

💵 $286.2k - $326.7k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Capital One

WebsiteLinkedInAll Job Openings

10,000+ employees

🏦 Banking

💳 Fintech

💸 Finance

💰 Post-IPO Equity on 2023-05

Banking • Fintech • Finance

Capital One is a leading financial services company that specializes in offering credit cards, auto loans, banking, and savings accounts. With a focus on innovation and technology, Capital One aims to change banking for good by providing customer-friendly solutions and fostering a diverse and inclusive workforce. The company is known for its commitment to creating a positive impact in the banking industry through advanced digital tools and customer service excellence.

📋 Description

• 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.

🏖️ Benefits

• Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being