Lead Machine Learning Engineer, Enterprise Platforms Technology

Capital One Financial

Confirmed live yesterday Low trust

Quick summary

Work type: On-site
Location: New York, NYMcLean, VA
Salary: $197,300–$225,100 / yr
Posted: 155 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $226k

This role $211k

$175k most similar roles pay here $274k

This role pays less than 62% of similar roles. Most pay $196,562–$254,750 — the shaded band above. At the midpoint, this role pays about $211k versus about $226k 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.

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View all roles at Capital One Financial

At a glance

TL;DR · Lead Machine Learning Engineer, Enterprise Platforms Technology

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Lead Machine Learning Engineer (Enterprise Platforms Technology) 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. The engineer will build and deliver models to solve real-world business problems, construct optimized data pipelines, and manage model retraining and monitoring in production. Key responsibilities include automating tests, managing code for security, and ensuring responsible and explainable AI practices. Candidates should possess expertise in Python, Scala, or Java, alongside experience with frameworks like scikit-learn, PyTorch, Dask, Spark, or TensorFlow. The role requires building cloud-based architectures on platforms such as AWS, Azure, or Google Cloud Platform to deliver optimized models while solving complex problems through robust application code and data preparation.

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 model and application code using Python, Scala, or Java.
Construct optimized data pipelines to feed machine learning models.
Retrain, maintain, and monitor machine learning models in production environments.
Build and manage cloud-based architectures to deliver optimized ML models at scale.
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 governance.

What we're looking for

Bachelor's degree required; Master's or Doctoral degree in a related 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.
At least 3 years of experience with industry-recognized ML frameworks like scikit-learn, PyTorch, Dask, Spark, or TensorFlow.
At least 3 years of experience building production-ready data pipelines for ML models.
At least 2 years of experience developing performant, resilient, and maintainable code.
Work authorization that require immigration support from an employer.

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