Lead Machine Learning Engineer; MLOps, KServe + building Kubernetes Clusters, PyTorch, TensorFl

Lead Machine Learning Engineer (MLOps, KServe + building Kubernetes Clusters, PyTorch, Tensor Flow on AWS)

As a Capital One Machine Learning Engineer (MLE), you’ll be part of an Agile team dedicated to product ionizing machine learning applications and systems ’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms, focusing on architectural design, developing model and application code, and ensuring high availability and performance of our ML applications.

The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand‑in‑hand with partners across the company to advance the state of the art in science and AI engineering, building and deploying proprietary solutions that drive value for millions of customers.

• Design, build, and/or deliver ML models and components that solve real‑world business problems, collaborating with Product and Data Science teams.
• Inform ML infrastructure decisions using your understanding of modeling techniques, including choice of model, data, feature selection, training, hyperparameter tuning, dimensionality reduction, bias/variance, and validation.
• Write and test application code, develop and validate ML models, and automate tests and deployment.
• Collaborate as part of a cross‑functional Agile team to create and enhance software that enables state‑of‑the‑art big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud‑based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of models and application code.
• Ensure code is well‑managed to reduce vulnerabilities; models are well‑governed from a risk perspective; practices follow Responsible and Explainable AI.
• Use programming languages such as Python, Scala, or Java.

• Bachelor’s degree
• At least 6 years of experience designing and building data‑intensive solutions using distributed computing (internship experience does not apply)
• At least 4 years of experience programming with Python, Scala, or Java
• At least 2 years of experience building, scaling, and optimizing ML systems

• Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
• 3+ years of experience building production‑ready data pipelines that feed ML models.
• 3+ years of on‑the‑job experience with an industry‑recognized ML framework such as scikit‑learn, PyTorch, Dask, Spark, or Tensor Flow.
• 2+ years of experience developing performant, resilient, and maintainable code.
• 2+ years of experience with data gathering and preparation for ML models.
• 2+ years of people‑leader experience.
• 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation.
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform.
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance.
• ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.

Capital One is an equal‑opportunity employer (EOE, including disability/veteran) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace.