This role pays more than
82%
of similar roles. Most pay
$187,850–$246,150
— the shaded band above.
At the midpoint, this role pays about
$246k
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
936 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 933 roles with salary data.
Machine Learning Engineer 5 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. Responsibilities include constructing optimized data pipelines, managing multi-tenant platforms for large-scale model training and serving, and ensuring models are governed through responsible and explainable AI practices. The candidate will write and test application code, automate deployments using CI/CD best practices, and monitor models in production environments. Technical requirements include proficiency in Python, Scala, or Java; experience with PyTorch, TensorFlow, Pandas, NumPy, and Scikit-learn; and expertise in distributed systems like Spark and Ray. Additionally, the role requires operating cloud-based architectures across AWS, GCP, or Azure using Kubernetes to manage containerized software systems.
What does a Machine Learning Engineer earn in New York?
Median $231150 from 51 postings across 15 companies.
Design, build, and deliver machine learning models and components to solve real-world business problems.
Build and scale multi-tenant platforms for large-scale ML model training and serving.
Develop and validate application code while automating tests and deployment processes.
Construct optimized data pipelines to feed and support machine learning models.
Retrain, maintain, and monitor models in production environments.
Implement cloud-based architectures and technologies to deliver optimized models at scale.
Ensure all models follow best practices for Responsible and Explainable AI.
What we're looking for
Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field.
At least 6 years of experience programming with Python, Java, Golang, or C++.
At least 6 years of Machine Learning experience using PyTorch, TensorFlow, and libraries like Pandas, NumPy, and Scikit-learn.
At least 6 years of experience operating large scale distributed systems such as Spark or Ray to prepare ML data.
At least 4 years of experience deploying and operating 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).
5+ years of experience optimizing ML algorithms, configurations, infrastructure, and following software development best practices like CI/CD (preferred).
5+ years of experience with advanced ML techniques, architectures, and building production-ready data pipelines (preferred).