Lead Machine Learning Engineer

Capital One Financial

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: New York, NY
Salary: $197,300–$225,100 / yr
Posted: 8 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 $219k

This role $211k

$175k most similar roles pay here $272k

This role pays less than 55% of similar roles. Most pay $192,050–$246,150 — the shaded band above. At the midpoint, this role pays about $211k versus about $219k 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

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Lead Machine Learning Engineer (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) joins the Marketing and Messaging team to develop scalable platforms for delivering hyper-personalized omnichannel messages across owned and paid Adtech channels. Working within an Agile framework, you will perform critical machine learning engineering activities including designing, building, and deploying ML models that solve real-world business problems. You will be responsible for constructing optimized data pipelines, managing model retraining and monitoring, and ensuring high availability through robust architectural design. The role requires expertise in Python, Scala, or Java, along with experience in AWS, SQL, and frameworks like scikit-learn, PyTorch, Dask, Spark, or TensorFlow. You will focus on building production-ready systems that prioritize responsible and explainable AI while managing the full lifecycle of model development, from feature selection and hyperparameter tuning to automated deployment and testing.

Skills

Python AWS SQL GenAI Scala Java PyTorch TensorFlow scikit-learn Spark Dask 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.
Inform infrastructure decisions based on model training, hyperparameter tuning, and feature selection.
Write and test application code to automate testing and deployment of ML models.
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 models at scale.
Implement CI/CD best practices, including test automation and monitoring for all code.
Ensure all models follow best practices for Responsible and Explainable AI.

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 machine learning systems.
At least 3 years of experience with industry-recognized ML frameworks like scikit-learn, PyTorch, Dask, Spark, or TensorFlow.
Experience developing and deploying machine learning solutions in public cloud environments such as AWS, Azure, or Google Cloud Platform.
Experience building production-ready data pipelines for machine learning models.
Work authorization that require immigration support from an employer.

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