As a Capital One Machine Learning Engineer (MLE),you'll be part of an Agile team dedicated to productionising 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.
Basic Qualifications:
Bachelors degree
At least 8 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 3 years of experience building, scaling and optimizing ML systems
At least 2 years of experience leading teams developing ML solutions
Preferred Qualifications:
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
4 years of on-the-job experience with industry-recognized ML frameworks such as sci-kit-learn, PyTorch, Dask, Spark, or TensorFlow
3 years of experience developing performant, resilient, and maintainable code
3 years of experience with data gathering and preparation for ML models
3 years of technical leadership experience
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
3 years of experience building production-ready data pipelines that feed ML models.
4 Years 
Salary not disclosed 
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