ML Engineer, Sr. (Remote-Eligible)

Capital One is a leading financial services company committed to transforming banking through innovative technology and responsible AI systems. With a strong emphasis on leveraging machine learning and data-driven solutions, Capital One has established itself as an industry pioneer in delivering personalized, real-time customer experiences. The company invests heavily in cutting-edge technology infrastructure and attracts world-class talent to maintain its leadership position. Capital One’s mission is to bring humanity and simplicity to banking by developing scalable AI solutions that enhance customer engagement, security, and operational efficiency. The organization fosters a culture of innovation, collaboration, and continuous learning, enabling it to stay at the forefront of technological advancements in the financial sector.

About the role

The Sr. Distinguished Machine Learning Engineer role at Capital One offers an exciting opportunity to lead the development and deployment of advanced AI and machine learning systems that power personalized customer experiences across multiple channels. This remote-eligible position involves defining the technical strategy and roadmap for the Personalization Platform, which is central to delivering real-time, tailored product interactions. The engineer will collaborate with cross-functional teams—including product managers, data scientists, cloud infrastructure specialists, and ML platform engineers—to build scalable, resilient, and low-latency systems.

Responsibilities

• Define and execute the technical strategy and roadmap for the Personalization Platform supporting real-time, multi-channel customer experiences
• Partner with cross-functional teams to develop advanced recommendation systems and algorithms
• Design, develop, and maintain a flexible, scalable rules engine for dynamic user segmentation and targeting
• Build and maintain robust ML infrastructure and pipelines encompassing feature extraction, model training, testing, deployment, and inference
• Architect low-latency, event-driven systems leveraging streaming data and contextual signals for real-time personalization
• Lead the evolution of MLOps practices by automating deployment workflows, validation, testing, and monitoring
• Introduce and implement state-of-the-art LLM optimization techniques to enhance performance, scalability, and cost-efficiency
• Leverage open-source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, and Nemo Guardrails
• Provide technical leadership, influence architecture standards, mentor engineers, and drive platform innovation organization-wide

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field
• Minimum of 10 years of experience designing and building data-intensive solutions using distributed computing
• At least 7 years of programming experience in C, C++, Python, or Scala
• Minimum of 4 years of experience with the full machine learning development lifecycle in a business-critical environment
• Experience deploying scalable AI solutions on major cloud platforms (AWS, GCP, Azure)
• Proficiency with ML frameworks such as PyTorch, TensorFlow, and orchestration tools like Databricks, Airflow, or Kubeflow
• Strong foundation in engineering and mathematics, with expertise in hardware and software optimization
• Experience with containerization (Docker, Kubernetes) and automated CI/CD pipelines
• Excellent communication skills and ability to articulate complex technical concepts
• Proven leadership in platform strategy and cross-functional collaboration

Benefits

• Competitive salary commensurate with experience and location
• Performance-based incentive compensation, including bonuses and long-term incentives
• Comprehensive health, dental, and vision insurance plans
• Retirement savings plans with company matching
• Generous paid time off and holiday policies
• Flexible work arrangements, including remote work options
• Opportunities for professional development and continuous learning
• Inclusive and diverse workplace environment