Lead Machine Learning Engineer

Capital One is seeking a Lead Machine Learning Engineer to productionize ML applications at scale, influence architectural design, and ensure high availability within an Agile team in New York, NY (onsite).

Responsibilities

• Design, build, and deploy ML models and components that address real world business needs, collaborating with Product and Data Science teams.
• Guide ML infrastructure decisions through your understanding of modeling techniques, including model choice, data and feature selection, training, hyperparameters, dimensionality, bias/variance, and validation.
• Address complex challenges by writing and testing application code, developing and validating ML models, and automating tests and deployment pipelines.
• Work within a cross functional Agile team to create and enhance software powering advanced big data and ML workloads.
• Retrain, monitor, and maintain models in production environments to sustain performance and reliability.
• Utilize or build cloud based architectures and platforms to deliver optimized ML models at scale.
• Construct efficient data pipelines to feed ML models with quality, timely data.
• Adopt continuous integration and continuous deployment practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure code quality, governance of models from risk perspective, and adherence to Responsible and Explainable AI best practices.
• Work with programming languages such as Golang, Python, Scala, or Java to implement solutions.

Requirements

• Bachelor's Degree required
• Minimum six years of experience designing and building data‑intensive solutions with distributed computing (internships not counted)
• At least four years of hands-on programming experience with Python, Scala, or Java
• Minimum 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 supporting overall well-being

Preferred Qualifications

• Master’s or Doctoral degree in computer science, electrical engineering, mathematics, or related field
• 3+ years building production-ready data pipelines that feed ML models
• 3+ years hands-on experience with industry leading ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
• 2+ years writing performant, resilient, and maintainable code
• 2+ years of experience gathering and preprocessing data for ML models
• 1+ years leading teams delivering ML solutions using established patterns and automation
• Experience deploying ML solutions in public cloud environments (AWS, Azure, or GCP)
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating performance
• Demonstrated ML industry impact through conferences, publications, blogs, open source, or patents
• Experience leveraging interactive AI tooling to boost productivity beyond basic code completion