This role pays more than
50%
of similar roles. Most pay
$192,050–$241,750
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$217k
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
836 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 829 roles with salary data.
Machine Learning Engineer 4 joins a collaborative team to drive major AI transformations and scale production models. This role involves designing, building, and delivering machine learning components that solve real-world business problems while collaborating with Product and Data Science teams. Daily responsibilities include writing and testing application code, automating deployments, constructing optimized data pipelines, and monitoring models in production. The work focuses on ensuring responsible and explainable AI through robust governance and high-quality software engineering. Candidates will utilize Python, Scala, or Java to develop solutions using frameworks like PyTorch and TensorFlow, along with libraries such as Pandas, NumPy, and Scikit-learn. Technical requirements include experience with distributed systems like Spark and Ray, cloud platforms including AWS, GCP, or Azure, and container orchestration via Kubernetes to manage large-scale machine learning applications in a production environment.
What does a Machine Learning Engineer earn in Virginia?
Median $211200 from 35 postings across 3 companies.
Design, build, and deliver machine learning models and components to solve real-world business problems.
Inform infrastructure decisions based on model training, hyperparameter tuning, feature selection, and validation techniques.
Write and test application code while automating tests and deployment processes for ML models.
Construct and optimize data pipelines to feed production-ready machine learning models.
Retrain, maintain, and monitor machine learning models in production environments.
Build or leverage cloud-based architectures to deliver optimized models at scale.
Implement CI/CD best practices and automated monitoring to ensure successful software deployment.
Ensure all code follows best practices for responsible, explainable AI and risk management.
What we're looking for
Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field.
At least 4 years of experience programming with Python, Java, Golang, or C++.
At least 4 years of Machine Learning experience using PyTorch, TensorFlow, and libraries like Pandas, NumPy, and Scikit-learn.
At least 4 years of experience operating large scale distributed systems such as Spark or Ray to prepare AI/ML data.
At least 2 years of experience deploying ML solutions in production using cloud platforms (AWS, GCP, Azure) and Kubernetes.
Master's or doctoral degree in computer science, electrical engineering, mathematics, or a related field (preferred).
3+ years of experience optimizing ML algorithms, configurations, infrastructure, and following software development best practices like CI/CD (preferred).
3+ years of experience with various ML techniques, model architectures, and designing production-ready data pipelines (preferred).