Lead Machine Learning Engineer

Capital One seeks a Lead Machine Learning Engineer in New York, NY (onsite) with a salary range of USD 215,200 to 245,600 per year to productionize ML at scale, shape architecture, and drive model and application code within an Agile team.

Responsibilities

• Design, develop, and deliver ML models and components that address real world business problems, collaborating with Product and Data Science teams
• Guide ML infrastructure choices based on modeling techniques, including model selection, data and feature engineering, training, hyperparameter tuning, dimensionality considerations, bias and variance, and validation
• Tackle complex problems by writing and testing application code, building and validating ML models, and automating tests and deployment
• Work within a cross functional Agile team to create software that powers advanced big data and ML applications
• Retrain, maintain, and monitor models in production environments
• Leverage or build cloud based architectures and platforms to deliver scalable ML models
• Construct optimized data pipelines to feed ML models
• Apply CI/CD best practices with test automation and monitoring to ensure successful deployment of ML models and code
• Ensure code quality, governance of models from risk perspective, and adherence to Responsible and Explainable AI practices
• Proficiency with Golang, Python, Scala, or Java

Requirements

• Bachelor’s Degree
• Minimum six years of experience designing and building data intensive solutions using distributed computing (internship experience does not apply)
• At least four years of experience programming with Python, Scala, or Java
• At least two years of experience building, scaling, and optimizing ML systems

Technologies

• Golang
• Python
• Scala
• Java
• scikit-learn
• PyTorch
• Dask
• Spark
• TensorFlow
• AWS
• Azure
• Google Cloud Platform

Benefits

• Performance-based incentive compensation (cash bonuses and/or long-term incentives)
• Health, financial and other benefits that support total well-being

Preferred Qualifications

• Master's or Doctoral degree in computer science, electrical engineering, mathematics, or a related field
• 3+ years building production ready data pipelines that feed ML models
• 3+ years experience with industry recognized ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
• 2+ years developing performant, resilient, and maintainable code
• 2+ years experience gathering and preparing data for ML models
• 1+ years leading teams developing ML solutions using industry best practices, patterns, and automation
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
• ML industry impact through conference presentations, papers, blogs, open source contributions, or patents
• Experience leveraging interactive AI tooling to accelerate productivity beyond basic code completion