Lead Machine Learning Engineer

$201,400 - $229,900/Yr

Capital One - McLean, VA

Full-time - Mid Level

McLean, VA

Credit Intermediation and Related Activities

About the position

As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. The Enterprise ML Tooling team at Capital One constructs AI servicing solutions to meet the needs of our customers. Our team is at the forefront of AI engineering at Capital One turning great research into usable product. You will work with researchers from the AI Foundations team in an effort to deploy, scale, experiment and validate extremely large multimodal models. 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.

Responsibilities

• 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 ML infrastructure decisions using 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.

Requirements

• Bachelor's degree
• At least 6 years of experience designing and building data-intensive solutions using distributed computing
• At least 4 years of experience programming with Python, Scala, or Java
• At least 2 years of experience building, scaling, and optimizing ML systems

Nice-to-haves

• 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

Benefits

• Comprehensive health benefits
• Financial benefits including performance-based incentives
• Inclusive benefits supporting total well-being