This role pays more than
57%
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 (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 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 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 solving complex business problems by implementing advanced algorithms and cloud-based architectures while ensuring models follow best practices in Responsible and Explainable AI within the enterprise platform space.
What does a Machine Learning Engineer earn in New York?
Median $230775 from 44 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-ready machine learning models.
Retrain, maintain, and monitor machine learning models in production environments.
Build and manage cloud-based architectures to deliver optimized models at scale.
Implement CI/CD best practices and monitoring to ensure successful deployment of application code.
Ensure all models follow best practices for Responsible and Explainable AI and risk governance.
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 building resilient software with CI/CD and monitoring (preferred).
3+ years of experience working with various ML techniques, architectures (RNNs, CNNs, LSTMs, Transformers), and training concepts (preferred).