Blackwomenintech
Lead Machine Learning Engineer
Blackwomenintech, Mc Lean, Virginia, us, 22107
Center 3 (19075), United States of America, McLean, VirginiaLead Machine Learning Engineer
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 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.What you'll do in the role:
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.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.At this time, Capital One will not sponsor a new applicant for employment authorization for this position.
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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 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.What you'll do in the role:
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.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.At this time, Capital One will not sponsor a new applicant for employment authorization for this position.
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