Lead Machine Learning Engineer

4 weeks ago


Capitol Heights, Maryland, United States Capital One Full time
Job Description

Capital One is seeking a highly skilled Lead Machine Learning Engineer to join our team. As a key member of our Agile team, you will be responsible for designing, building, and deploying machine learning applications and systems at scale.

Key Responsibilities:
  • Design and develop machine learning models and components that solve real-world business problems.
  • Collaborate with Product and Data Science teams to inform ML infrastructure decisions.
  • 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.
Requirements:
  • Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
  • 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.
Preferred Qualifications:
  • Master's or doctoral degree in Computer Science, Electrical Engineering, Mathematics, or a related 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.


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