This role pays less than
76%
of similar roles. Most pay
$195,000–$254,750
— the shaded band above.
At the midpoint, this role pays about
$192k
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. This role involves technical design, developing and reviewing model code, and ensuring high availability for machine learning applications. The engineer will build and deliver models to solve real-world business problems, construct optimized data pipelines, and manage infrastructure decisions regarding feature selection, hyperparameter tuning, and validation. Key responsibilities include automating tests, monitoring production models, and implementing responsible and explainable AI practices. Candidates should possess experience with Python, Scala, or Java, along with proficiency in frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow. The role involves developing cloud-based architectures to deliver optimized models while managing complex data pipelines within a high-performance environment to solve technical challenges related to big data and machine learning.
Design, build, and deliver machine learning models and components that solve real-world business problems.
Develop and review model and application code while ensuring high availability and performance.
Construct optimized data pipelines to feed machine learning models.
Retrain, maintain, and monitor machine learning models in production environments.
Build and manage cloud-based architectures and technologies to deliver optimized models at scale.
Implement CI/CD best practices, including test automation and monitoring, for model deployment.
Ensure all code is well-managed to reduce vulnerabilities and follow Responsible and Explainable AI standards.
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; and 2+ years of people leader experience (preferred).