Machine Learning Engineer — United States — LinkedIn — 4489360071

About The Company

Capital One is a technology-driven financial services company focused on solving real customer needs through innovation. Its teams bring together makers, builders, and problem-solvers who work collaboratively in an inclusive, iterative environment. Capital One invests in modern technology and artificial intelligence to improve products, services, and business operations. The Enterprise Machine Learning Platform team provides the infrastructure and tools that enable teams across the company to develop and operate machine learning workflows at scale.

About The Role

Capital One is seeking a Machine Learning Engineer 4 to help build and evolve its high-throughput, multi-tenant machine learning training platform. This individual contributor role supports workflows spanning classical machine learning, generative uq fine-tuning, and distributed computing with Ray, Spark, and Dask. The team owns the Kubeflow Pipelines v2 application layer on Amazon EKS and develops internal Python and Go SDKs, custom Kubernetes operators, and developer tools that simplify complex infrastructure for critical, regulated workloads.

You will work across machine learning engineering, data engineering, modeling, and operations. You will help integrate agentic uq into modern engineering lifecycles and contribute to upstream open-source Kubeflow while balancing timely feature delivery with enterprise-level reliability, governance, and security.

Responsibilities

Design, build, test, and deliver machine learning models, platform components, and software that address business needs, partnering with Product and Data Science teams. Apply knowledge of modeling methods and practical challenges—including data and feature selection, model choice, training, hyperparameter tuning, dimensionality, bias and variance, and validation—to guide infrastructure and implementation decisions.

Develop application code, validate models, automate testing, and support deployment across cloud-based and distributed environments. Build and optimize data pipelines for model training and inference, and use continuous integration and delivery practices to improve release quality. Retrain, maintain, and monitor models and services in production. Collaborate with a cross-functional Agile team to develop scalable big data and machine learning applications, and promote secure, well-managed code, sound model governance, and Responsible and Explainable uq practices. Use languages such as Python, Java, Scala, or Go as appropriate to the work.

Qualifications

Applicants must have a bachelor’s degree or higher in Computer Science, Machine Learning, or a related quantitative field, such as Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering. Required experience includes at least four years programming in Python, Java, Go, or C++; four years applying machine learning with frameworks such as PyTorch or TensorFlow and libraries such as Pandas, NumPy, or scikit-learn; and four years using large-scale distributed systems, including Spark or Ray, to prepare machine learning data. Candidates also need at least two years deploying and operating production machine learning solutions in a cloud environment and using Kubernetes for large-scale containerized systems.

A master’s or doctoral degree is preferred. Preferred experience includes designing, training, and evaluating modern machine learning models; architecting end-to-end distributed machine learning systems; managing workloads with Docker, Kubernetes, and orchestration tools such as Kubeflow; and applying CI/CD, automated testing, code reviews, monitoring, alerting, incident planning, and progressive rollout strategies. Technical leadership experience and authorship or co-authorship of a paper on a machine learning technique, model, or proof of concept are also valued.

Benefits

The full-time annual salary ranges are $197,300–$225,100 in McLean, Virginia, and $215,200–$245,600 in New York, New York, and San Jose, California. Pay for other locations is based on the range associated with the work location; the offer letter will state the actual salary. This role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives. Capital One offers a competitive benefits package supporting health, financial, and overall well-being. Eligibility varies by employment status and management level. Applications are expected to be accepted for at least five business days.

Capital One will not sponsor new employment authorization or provide immigration-related support for this position. No agencies, please.

Equal Opportunity

Capital One is an equal opportunity employer, including for disability and veteran status, and is committed to nondiscrimination under applicable laws. The company maintains a drug-free workplace and considers qualified applicants with criminal histories consistent with applicable federal, state, and local laws. Accommodation requests related to applying or accessing employment information may be directed to Capital One Recruiting. Information submitted for an accommodation will be kept confidential and used only as needed to provide reasonable assistance.