Lead Machine Learning Engineer (Intelligent Foundations and Experiences)

Lead Machine Learning Engineer (Intelligent Foundations and Experiences)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.Team Info: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 develop workflows in Kubernetes-based platforms, including KubeFlow Pipelines, 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!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 degreeAt 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 JavaAt least 2 years of experience building, scaling, and optimizing ML systemsPreferred Qualifications:Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field3+ years of experience building production-ready data pipelines that feed ML models3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow3+ years of experience with Kubernetes or KubeFlow Pipelines2+ years of experience developing performant, resilient, and maintainable code2+ years of experience with data gathering and preparation for ML models2+ years of people leader experience1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automationExperience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud PlatformExperience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performanceML industry impact through conference presentations, papers, blog posts, open source contributions, or patents

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