Lead Machine Learning Engineer (Enterprise Platforms Technology)

Capital One offers a performance based incentive compensation package that may include cash bonuses and long-term incentives, along with health, financial, and other benefits designed for your total well-being. This onsite role lives within a collaborative, engineering-driven environment that values scalable machine learning platforms and responsible AI practices, all focused on productionizing ML solutions at scale in McLean, Virginia.

Location and compensation

Location: McLean, VA — onsite. Salary: USD 197,300–225,100 per year.

Responsibilities

• Design, build, and deliver ML models and components that address real business needs, collaborating with Product and Data Science teams.
• Guide ML infrastructure decisions based on modeling techniques and considerations such as model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation.
• Address complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Work within a cross-functional Agile team to create and enhance software powering state-of-the-art big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Apply CI/CD best practices, including test automation and monitoring, to ensure reliable deployment of ML models and application code.
• Maintain well-governed code and models to manage risk, and adhere to Responsible and Explainable AI practices.
• Demonstrate proficiency with programming languages such as Python, Scala, or Java.

Requirements

• Bachelor’s Degree
• At least 6 years of experience designing and building data-intensive solutions using distributed computing ( internship experience does not apply )
• At least 4 years of experience programming with Python, Scala, or Java
• At least 2 years of experience building, scaling, and optimizing ML systems

Technologies

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