This role pays less than
59%
of similar roles. Most pay
$194,850–$254,750
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$225k
for comparable roles.
Based on 240 similar postings.
Employer
About Capital One Financial
Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking
Capital One Financial currently has
998 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 992 roles with salary data.
Lead Machine Learning Engineer
As part of an Agile team, the Lead Machine Learning Engineer will focus on productionizing machine learning applications and systems at scale. The role involves technical design, developing and reviewing model code, and ensuring high availability for machine learning applications. Key responsibilities include building models to solve real-world business problems, making informed infrastructure decisions regarding feature selection and hyperparameter tuning, and constructing optimized data pipelines. The candidate will also be responsible for retraining, monitoring, and governing models for risk and explainability. Required technical skills include proficiency in Python, Scala, or Java, along with experience using frameworks like scikit-learn, PyTorch, Dask, Spark, or TensorFlow. The role involves building cloud-based architectures to deliver optimized models while utilizing continuous integration and deployment best practices to manage code quality and automate testing for large-scale data applications.
Design, build, and deliver machine learning models and components that solve real-world business problems.
Develop and review high-quality model and application code using Python, Scala, or Java.
Construct and optimize data pipelines to feed machine learning models at scale.
Retrain, maintain, and monitor machine learning models in production environments.
Build and manage cloud-based architectures to deliver optimized ML models.
Implement CI/CD best practices, including test automation and monitoring, for model deployment.
Ensure all models follow best practices for Responsible and Explainable AI.
What we're looking for
At least 6 years of experience designing and building data-intensive solutions using distributed computing.
At least 4 years of experience programming with Python, Scala, or Java.
At least 2 years of experience building, scaling, and optimizing ML systems.
Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field (preferred).
3+ years of experience building production-ready data pipelines that feed ML models (preferred).
3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (preferred).
2+ years of experience developing performant, resilient, and maintainable code; 2+ years of experience with data gathering and preparation for ML models (preferred).