Lead Machine Learning

Capital One-posted 4 months ago
$175,800 - $200,700/Yr
Full-time • Senior
Riverwoods, IL
Credit Intermediation and Related Activities
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As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team building models and productionizing machine learning applications and systems at scale providing Kafka infrastructure to support an event-driven architecture, developing DevOps best practices and the development of tools to facilitate cloud-based application development. 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.

  • 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.
  • Guide early-career engineers by providing learning tasks as well as work-related tasks, directs the work of emerging talent, and helps them continue to grow in their technical skillset through mentorship.
  • Operate independently, investigate solutions deeply, and see a task through from planning and design to deployment and adoption.
  • Switch work priorities as new initiatives emerge, embrace change, and effectively communicate complex ideas while actively listening to feedback.
  • Use critical thinking to question assumptions and improve processes, and has a passion for learning new technologies, creating proof-of-concepts, and educating others.
  • 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
  • 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 experience leading teams developing ML solutions using industry best practices, patterns, and automation
  • 2+ years experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
  • 2+ Years Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
  • 2+ years experience with event driven architecture such as Kafka
  • Comprehensive health benefits
  • Financial benefits including performance-based incentive compensation
  • Inclusive set of benefits that support total well-being
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