Machine Learning Engineer 4 - Finance Tech

We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team.

As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.

Finance Tech is responsible for transforming Capital One's financial engine into the most trusted, nimble, and insightful in the industry.

What You’ll Do:

• Design, build, and 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 your 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
• 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
• 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
• Use programming languages like Python, Scala, or Java

Basic Qualifications:

• Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field
• At least 4 years of experience programming with Python, Java, Golang, or C++
• At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries
• At least 4 years of experience using and operating large scale distributed systems
• At least 2 years of experience deploying and operating Machine Learning solutions in production

Preferred Qualifications:

• Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
• 3+ years of experience optimizing ML algorithms, configurations, and infrastructure
• 3+ years of experience following software development best practices
• 3+ years of experience building resilient software solutions
• 3+ years of experience working with Machine Learning techniques
• 3+ years of experience designing, implementing, and scaling production-ready data pipelines
• 1+ years of experience as a technical lead developing ML solutions
• Authored/co-authored a paper on a ML technique, model, or proof of concept

The minimum and maximum full-time annual salaries for this role are: Cambridge, MA: $197,300 - $225,100 McLean, VA: $197,300 - $225,100 New York, NY: $215,200 - $245,600