Lead Machine Learning Engineer

Capital One Financial

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: Chicago, IL
Salary: $179,400–$204,700 / yr
Posted: 18 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $225k

This role $192k

$169k most similar roles pay here $273k

This role pays less than 76% of similar roles. Most pay $195,000–$254,750 — the shaded band above. At the midpoint, this role pays about $192k 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 · Lead Machine Learning Engineer

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Lead Machine Learning Engineer joins an Agile team focused on productionizing machine learning applications and systems at scale. This role involves technical design, development, and implementation of ML applications using emerging technology platforms while ensuring high availability and performance. Key responsibilities include building models to solve real-world business problems, managing infrastructure decisions like feature selection and hyperparameter tuning, constructing optimized data pipelines, and automating deployments through CI/CD best practices. The engineer will also focus on responsible and explainable AI while maintaining models in production. Required technical skills include proficiency in Python, Scala, or Java, along with experience using frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow. The role involves building cloud-based architectures to deliver optimized models and managing complex data pipelines within a collaborative, cross-functional environment to solve large-scale machine learning challenges.

Skills

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

What you'll do

Design, build, and deliver machine learning models and components that solve real-world business problems.
Develop and review high-quality model and application code using Python, Scala, or Java.
Construct optimized data pipelines to feed machine learning models at scale.
Retrain, maintain, and monitor machine learning models in production environments.
Build and manage cloud-based architectures and technologies to deliver optimized ML solutions.
Implement CI/CD best practices, including test automation and monitoring, for model deployment.
Ensure all code follows best practices for Responsible and Explainable AI and risk management.

What we're looking for

Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field (preferred).
At least 6 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 2 years of experience building, scaling, and optimizing ML systems.
3+ years of experience building production-ready data pipelines that feed ML models (preferred).
3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (preferred).
2+ years of experience developing performant, resilient, and maintainable code (preferred).

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