This role pays more than
62%
of similar roles. Most pay
$192,050–$237,837
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$215k
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 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) joins the Marketing and Messaging team to build scalable platforms for hyper-personalized omnichannel messages across owned and paid Adtech channels. This role involves designing, building, and delivering machine learning models and components that solve real-world business problems while collaborating with Product and Data Science teams. Day-to-day responsibilities include writing application code, automating tests and deployments, constructing optimized data pipelines, and monitoring models in production. The candidate will work within an Agile framework to develop software for big data applications using Python, Scala, or Java. Technical requirements include experience with PyTorch, TensorFlow, Pandas, NumPy, Scikit-learn, Spark, Ray, and Kubernetes. The role focuses on the technical challenge of scaling production models and ensuring responsible, explainable AI within a complex enterprise infrastructure environment.
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.
Inform infrastructure decisions based on model training, feature selection, hyperparameter tuning, 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-level machine learning models.
Retrain, maintain, and monitor models in production environments to ensure consistent performance.
Build and manage cloud-based architectures to deliver optimized models at scale.
Implement CI/CD best practices, including test automation and monitoring, for all model deployments.
Ensure all code and 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 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, infrastructure, and following software development best practices like CI/CD (preferred).
3+ years of experience with advanced ML techniques, architectures (RNNs, CNNs, Transformers), and building production-ready data pipelines (preferred).