Lead Machine Learning Engineer

Capital One Financial

Confirmed live today Low trust

Quick summary

Work type
On-site
Location
McLean, VAPlano, TX
Employment
Full-time
Posted
47 days ago
Freshness
Confirmed live today

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Salary context

How this pay compares to similar roles

Similar $228k
$173k $289k
below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $200,437–$254,750.

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 2107 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1107 roles with salary data.

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At a glance

TL;DR · Lead Machine Learning Engineer

The Lead Machine Learning Engineer joins an Agile team focused on productionizing machine learning applications and systems at scale. This role involves participating in technical design, developing and reviewing model code, and ensuring high availability and performance of production applications. Key responsibilities include building ML models to solve real-world business problems, constructing optimized data pipelines, and automating deployment through continuous integration and continuous deployment best practices. The engineer will manage model retraining, monitoring, and governance while adhering to Responsible and Explainable AI standards. Required technical skills include proficiency in Python, Scala, or Java, along with experience in distributed computing and ML frameworks like scikit-learn, PyTorch, Dask, Spark, or TensorFlow. The work involves building cloud-based architectures to deliver optimized models and managing complex data-intensive solutions.

What you'll do

  • Design, build, and deliver machine learning models and components to solve real-world business problems.
  • Write and test application code to solve complex problems and automate deployment processes.
  • Construct and optimize data pipelines to feed machine learning models.
  • Retrain, maintain, and monitor machine learning models in production environments.
  • Build and leverage cloud-based architectures to deliver optimized ML models at scale.
  • Implement continuous integration and continuous deployment best practices, including test automation and monitoring.
  • Ensure all code and models follow best practices for Responsible and Explainable AI.
  • Inform infrastructure decisions regarding model selection, data features, hyperparameter tuning, and validation.

What we're looking for

  • 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.
  • Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field (preferred).
  • 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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