Senior Lead Machine Learning Engineer

Capital One Financial

Confirmed live today Low trust

Quick summary

Work type: On-site
Location: McLean, VA
Employment: Full-time
Posted: 104 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $229k

$173k $289k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $203,900–$254,750.

Based on 240 similar postings.

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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 · Senior Lead Machine Learning Engineer

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The Senior Lead Machine Learning Engineer joins the Customer Intelligent Decisions & Experiences team to productionize machine learning applications at scale. You will lead a team in designing, building, and maintaining large-scale, Reinforcement Learning-based recommender systems that power personalized experiences across marketing, customer servicing, and digital products. Daily responsibilities include developing ML models, constructing optimized data pipelines, and managing cloud-based architectures to deliver real-time digital experiences. You will ensure model governance, follow Responsible and Explainable AI best practices, and automate deployment using continuous integration and continuous deployment. The role requires proficiency in Python, Scala, or Java, along with experience in frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow. You will solve complex problems by bridging consumer personalization models with robust infrastructure engineering.

Skills

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

What you'll do

Design, build, and deliver machine learning models and components to solve real-world business problems.
Inform infrastructure decisions regarding model selection, data features, hyperparameter tuning, and validation.
Write and test application code to solve complex problems and automate deployment processes.
Retrain, maintain, and monitor machine learning models in production environments.
Build and manage cloud-based architectures and platforms to deliver optimized models at scale.
Construct optimized data pipelines to feed machine learning models.
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.

What we're looking for

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.
Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field (preferred).
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform (preferred).
4+ years of experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (preferred).