Sr. Distinguished Machine Learning Engineer (remote-eligible)

Capital One Capital One · Banking · McLean, VA +1 · Remote

This role focuses on building and scaling the intelligence and infrastructure for real-time, personalized customer experiences using ML and GenAI systems. It involves defining technical strategy, partnering with data science and ML platform teams, developing a rules engine, building ML infrastructure for end-to-end workflows, architecting low-latency event-driven systems, driving MLOps, and optimizing LLM performance for production AI systems. The role also involves providing technical leadership and leveraging various AI technologies.

What you'd actually do

  1. Define and drive technical strategy and roadmap for our Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across all Capital One products and services.
  2. Partner cross-functionally with Product, Data science, Cloud infrastructure, and Machine learning platform teams to align on and co-develop the advanced recommendation systems and algorithms serving our Capital One users.
  3. Develop and maintain a flexible, scalable rules engine to enable business-driven personalization logic, allowing dynamic configuration of user segmentation, targeting rules, and real-time decisioning while integrating seamlessly with ML-driven recommendations.
  4. Design, build and maintain robust ML infrastructure and pipelines to support end-to-end workflows including feature extraction, model training, testing, guardrails, model evaluation, deployment, and both real-time and batch inference - ensuring high performance, scalability, and reliability.
  5. Architect low-latency, event-driven systems for enabling real-time dynamic personalization and decisioning based on streaming data, user behavior, and contextual signals.

Skills

Required

  • designing and building data-intensive solutions using distributed computing
  • programming in C, C++, Python, or Scala
  • full ML development lifecycle using modern technology in a business critical setting
  • deploying scalable, responsible AI solutions on major cloud platforms (AWS, GCP, Azure)
  • designing, implementing and scaling personalization platform and recommendation systems
  • Python, Java, C++, or Golang
  • ML frameworks (PyTorch, TensorFlow)
  • orchestration

Nice to have

  • Master's or PhD in Computer Science or a relevant technical field
  • Feed Personalization/Ads Ranking/Targeted Marketing Messaging

What the JD emphasized

  • real-time
  • personalization
  • ML infrastructure
  • low-latency
  • LLM optimization

Other signals

  • building intelligence and infrastructure for individualized, real-time customer experiences
  • production-grade ML and GenAI systems
  • low-latency application platforms
  • real-time dynamic personalization and decisioning
  • LLM optimization techniques