Machine Learning Engineer IV

Capital One Financial

Confirmed live today Low trust

Quick summary

Work type: On-site
Location: McLean, VAPlano, TX
Salary: $179,400–$204,700 / yr
Posted: 2 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 $217k

This role $192k

$170k most similar roles pay here $269k

This role pays less than 70% of similar roles. Most pay $192,050–$241,750 — the shaded band above. At the midpoint, this role pays about $192k 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.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Machine Learning Engineer IV

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As a Machine Learning Engineer 4, you will join a collaborative team to drive major AI transformations and scale production models. You will design, build, and deliver machine learning components that solve real-world business problems while collaborating with Product and Data Science teams. Your daily responsibilities include writing and testing application code, developing and validating models, automating deployments, and constructing optimized data pipelines. You will manage model retraining, monitoring, and governance to ensure responsible and explainable AI. The role requires proficiency in Python, Scala, or Java, along with experience in PyTorch, TensorFlow, Pandas, NumPy, and Scikit-learn. Additionally, you will utilize large-scale distributed systems like Spark and Ray, while operating cloud-based infrastructure across AWS, GCP, or Azure using Kubernetes to manage containerized software for big data and machine learning applications.

What does a Machine Learning Engineer earn in Virginia?

Median $211200 from 35 postings across 3 companies.

See salary data

Skills

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

What you'll do

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 and support large-scale machine learning models.
Retrain, maintain, and monitor machine learning models in production environments.
Build or leverage cloud-based architectures and technologies to deliver optimized models at scale.
Implement CI/CD best practices and automated monitoring to ensure successful deployment of code and models.
Ensure all models follow best practices for Responsible and Explainable AI and meet risk governance standards.

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 various ML techniques, architectures, and designing production-ready data pipelines (preferred).

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