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.
As a Lead Machine Learning Engineer, you will join an Agile team dedicated to productionizing machine learning applications and systems at scale. You will be responsible for the technical design, development, and implementation of machine learning architectures while ensuring high availability and performance. Your daily work involves building and delivering models that solve real-world business problems, constructing optimized data pipelines, and managing model retraining and monitoring in production environments. To succeed, you must develop and validate application code while adhering to principles of Responsible and Explainable AI. The role requires proficiency in Python, Scala, or Java, along with experience using frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow. You will also leverage cloud-based architectures and CI/CD best practices to deliver scalable solutions within a complex technical ecosystem.
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 optimized 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 and technologies to deliver optimized ML models.
Implement CI/CD best practices, including test automation and monitoring, for model deployment.
Ensure all code follows best practices for Responsible and Explainable AI.
Inform infrastructure decisions based on modeling techniques like feature selection, hyperparameter tuning, and validation.
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).