Senior Machine Learning Engineer - Capital One Software (Remote)

$140,000 - $159,800/Yr

Capital One - McLean, VA

Full-time - Senior

Remote - McLean, VA

Credit Intermediation and Related Activities

About the position

As a Senior Machine Learning Engineer at Capital One, you will play a pivotal role in building and deploying machine learning models and components that address real-world business challenges. This position involves collaboration with product and data science teams to design and implement ML solutions, optimize data pipelines, and ensure the responsible use of AI technologies. You will be part of a cross-functional Agile team, contributing to the development of state-of-the-art big data and ML applications while maintaining and monitoring models in production.

Responsibilities

• Design, build, and deliver ML models and components that solve real-world business problems.
• Collaborate with Product and Data Science teams to inform ML infrastructure decisions.
• Write and test application code, develop and validate ML models, and automate tests and deployment.
• Collaborate as part of a cross-functional Agile team to enhance software for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Implement continuous integration and continuous deployment best practices for ML models and application code.
• Ensure code is well-managed to reduce vulnerabilities and models are well-governed from a risk perspective.

Requirements

• Bachelor's degree in a relevant field.
• At least 4 years of experience programming with Python, Scala, or Java.
• At least 3 years of experience designing and building data-intensive solutions using distributed computing.
• At least 2 years of experience with industry-recognized ML frameworks (e.g., scikit-learn, PyTorch, Dask, Spark, TensorFlow).
• At least 1 year of experience productionizing, monitoring, and maintaining models.

Nice-to-haves

• 1+ years of experience building, scaling, and optimizing ML systems.
• 1+ years of experience with data gathering and preparation for ML models.
• 2+ years of experience developing performant, resilient, and maintainable code.
• Experience developing and deploying ML solutions in a public cloud (AWS, Azure, Google Cloud Platform).
• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
• 3+ years of experience with distributed file systems or multi-node database paradigms.
• Contributed to open source ML software.
• Authored/co-authored a paper on a ML technique, model, or proof of concept.
• 3+ years of experience building production-ready data pipelines that feed ML models.

Benefits

• Comprehensive health insurance coverage.
• Financial benefits including performance-based incentives and bonuses.
• Support for total well-being through various programs.