Machine Learning Engineer - Manager IC

Machine Learning Engineer 4 - Manager IC

Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team.

As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.

As part of the Enterprise ML Platform team, you will build and evolve the high-throughput, multi-tenant training platform that powers model workflows across Capital One—ranging from classical ML to GenAI fine-tuning and distributed computing with Ray, Spark, and Dask. Owning the Kubeflow Pipelines v2 application layer on Amazon EKS, the team develops internal Python and Go SDKs, custom Kubernetes operators, and developer tooling that abstract complex infrastructure for critical, regulated workloads.

In this role, you will integrate agentic AI into modern engineering life cycles and contribute directly to upstream open-source Kubeflow, balancing fast-paced feature delivery with enterprise-grade platform stability and security.

What You’ll Do:

• The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
• Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
• Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation)
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating 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
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
• Use programming languages like Python, Scala, or Java

Basic Qualifications:

• Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• At least 4 years of experience programming with Python, Java, Golang, or C++
• At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, Num Py, Scikit-learn)
• At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data
• At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using…