Highly Experienced Machine Learning Engineer Wanted for Capital One's AI Team

6 days ago


Cambridge, Massachusetts, United States Capital One Full time
About the Role

We are seeking a highly skilled and experienced Machine Learning Engineer to join our team at Capital One, working on cutting-edge Artificial Intelligence projects. This role involves designing, building, and deploying machine learning models and components that solve real-world business problems.

As a Machine Learning Engineer, you will collaborate with cross-functional teams, including Product and Data Science, to develop and implement innovative solutions using industry-recognized frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow.

Responsibilities
  • Design, build, and deliver ML models and components that solve real-world business problems
  • Inform infrastructure decisions using understanding of ML modeling techniques and issues
  • 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
About Us

Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex (including pregnancy, childbirth or related medical conditions), race, color, age, national origin, religion, disability, genetic information, marital status, sexual orientation, gender identity, gender reassignment, citizenship, immigration status, protected veteran status, or any other basis prohibited under applicable federal, state or local law.

This role is expected to accept applications for a minimum of 5 business days. The salary for this position is $201,400 - $229,900 per year, based on location. Please note that salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.



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