Machine Learning Engineer Manager

Capital One Financial

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Chicago, ILNew York, NY
Salary: $179,400–$204,700 / yr
Employment: Full-time
Posted: 9 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $222k

This role $192k

$169k most similar roles pay here $272k

This role pays less than 77% of similar roles. Most pay $200,206–$243,600 — the shaded band above. At the midpoint, this role pays about $192k versus about $222k 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 1554 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 796 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Machine Learning Engineer Manager

Role Posting Log in to save

The Machine Learning Engineer 4 (Manager, IC) 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 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, and Ray. Additionally, the role requires expertise in cloud platforms like AWS, GCP, or Azure, along with Kubernetes for containerized systems. The work focuses on creating responsible and explainable AI while managing model governance and infrastructure at scale.

Skills

Python PyTorch TensorFlow Scikit-learn NumPy Pandas Spark Ray Kubernetes AWS GCP Azure CI/CD Java Scala Golang C++ Machine Learning Data Pipelines

What you'll do

Design, build, and deliver machine learning models and components that solve real-world business problems.
Inform infrastructure decisions based on model training, hyperparameter tuning, feature selection, and validation techniques.
Write and test application code to develop, validate, and automate the deployment of ML models.
Construct and optimize data pipelines to feed large-scale 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 all model deployments.
Ensure all code follows best practices for responsible AI, risk governance, and security.

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 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, and infrastructure (preferred).
3+ years of experience following software development best practices including source control, testing, code reviews, and CI/CD (preferred).

Save