Senior Lead Machine Learning Engineer

Capital One Financial

Confirmed live yesterday Low trust

Quick summary

Work type: On-site
Location: McLean, VA
Salary: $229,900–$262,400 / yr
Posted: 52 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $225k

This role $246k

$175k most similar roles pay here $275k

This role pays more than 68% of similar roles. Most pay $195,112–$254,750 — the shaded band above. At the midpoint, this role pays about $246k versus about $225k 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 998 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 992 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Senior Lead Machine Learning Engineer

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As a Sr. Lead Machine Learning Engineer, you will join an Agile team focused on productionizing machine learning applications and systems at scale. You will be responsible for the technical design, development, and implementation of machine learning architectures while ensuring high availability and performance. Your daily work involves building and delivering models to solve real-world business problems, constructing optimized data pipelines, and managing model retraining and monitoring in production environments. You will collaborate with cross-functional teams to develop software for big data applications using Python, Scala, or Java. The role requires expertise in ML frameworks like scikit-learn, PyTorch, Dask, Spark, or XGboost, alongside experience with cloud platforms such as AWS, Azure, or Google Cloud Platform. You will ensure all code follows best practices for responsible and explainable AI while managing risks and vulnerabilities.

Skills

Machine Learning Python Scala Java AWS Azure Google Cloud Platform scikit-learn PyTorch Dask Spark XGBoost Data Pipelines Distributed Computing Agile

What you'll do

Design, build, and deliver machine learning models and components that solve real-world business problems.
Develop and review model code while ensuring high availability and performance of production applications.
Construct optimized data pipelines to feed 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, including test automation and monitoring, for model deployment.
Ensure all code is well-managed to reduce vulnerabilities and follow Responsible and Explainable AI practices.
Inform infrastructure decisions using knowledge of modeling techniques like hyperparameter tuning and feature selection.

What we're looking for

Bachelor's degree required; Master's or Doctoral degree in a related field preferred.
At least 8 years of experience designing and building data-intensive solutions using distributed computing.
At least 4 years of experience programming with Python, Scala, or Java.
At least 3 years of experience building, scaling, and optimizing ML systems.
At least 2 years of experience leading teams developing ML solutions.
At least 4 years of experience with industry-recognized ML frameworks like scikit-learn, PyTorch, Dask, Spark, or XGboost.
At least 3 years of experience building production-ready data pipelines and preparing data for ML models.
Experience deploying ML solutions in public clouds such as AWS, Azure, or Google Cloud Platform.

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