Senior Machine Learning Engineer

$174,900 - $199,700/Yr

Capital One - Cambridge, MA

Full-time - Mid Level

Cambridge, MA

Credit Intermediation and Related Activities

About the position

As a Senior Machine Learning Engineer at Capital One, you will be responsible for designing, developing, and implementing machine learning applications at scale. This role involves collaborating with cross-functional teams to solve real-world business problems using ML models and components, as well as making infrastructure decisions, model training, deployment, and monitoring.

Responsibilities

• Design, build, and deliver ML models and components that solve real-world business problems.
• Inform ML 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 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.
• Leverage continuous integration and continuous deployment best practices to ensure successful deployment of ML models and application code.
• Ensure all code is well-managed to reduce vulnerabilities and that models are well-governed from a risk perspective.

Requirements

• Bachelor's degree
• 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 (scikit-learn, PyTorch, Dask, Spark, or 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 such as AWS, Azure, or 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
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance

Benefits

• Comprehensive health benefits
• Financial benefits including performance-based incentives
• Inclusive set of benefits supporting total well-being