Capital One Lead Machine Learning Engineer 

United States, Virginia, Arlington 

720877560

Center 3 (19075), United States of America, McLean, Virginia Lead Machine Learning Engineer
The Enterprise ML Libraries & Tools (EMLT) Workflows team provides the last-mile tooling needed to enable Data Scientists to develop and deploy workflow pipelines on our Enterprise ML Platform (EMP). We work directly with our DS customers on many of the most critical credit, fraud, and decisioning models used to ensure they can onboard to our enterprise offerings and accelerate the adoption of AI/ML at scale. As a Lead MLE, you will support and develop workflows in Kubernetes-based platforms, including Kubeflow pipelines/components, and scale out big-data workloads using Spark and Dask. If you enjoy working in a highly collaborative environment and implementing leading-edge technologies and AI/ML algorithms to solve complex business problems then this is the group for you.

• Partner with teams of data scientists, machine learning engineers and software reliability experts to deploy custom large language, sequence, graph and traditional machine learning models
• Use a broad set of technologies - PyTorch, HuggingFace, AWS SageMaker, Kubernetes, and Apache Spark in an effort to scale up existing models to support millions of customers
• Support the AI Foundations team with engineering expertise and in ad-hoc development operations support
• Design and build automated solutions to manual machine learning processes in order to move research products into deployed models
• Interface with enterprise platform owners and senior engineering leadership in order to build and manage existing and upcoming solutions

What you’ll do in the role:

• 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
• 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

Preferred Qualifications:

• 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 TensorFlow
• 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

New York City (Hybrid On-Site): $201,400 - $229,900 for Lead Machine Learning EngineerThis role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.