Lead Machine Learning Engineer, Finance Tech - AI Enablement

Capital One Financial

Confirmed live 2 days ago Low trust

Quick summary

Work type: On-site
Location: Cambridge, MANew York, NYMcLean, VA
Salary: $197,300–$225,100 / yr
Posted: 59 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $226k

This role $211k

$175k most similar roles pay here $273k

This role pays less than 59% 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, Finance Tech - AI Enablement

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Lead Machine Learning Engineer (Finance Tech - AI Enablement) joins the Finance Tech horizontal AI enablement team to develop and deploy best practices for end-user facing AI use cases within the Finance line of business. This role involves collaborating with cross-functional teams to build AI-powered products, designing and testing software components including large language model inference, similarity search, guardrails, and agentic AI. The engineer will fine-tune foundation models, construct optimized data pipelines, and manage model monitoring in production while ensuring responsible and explainable AI practices. Key technical requirements include proficiency in Python, Scala, or Java, along with experience in scikit-learn, PyTorch, Dask, Spark, or TensorFlow. The role focuses on solving complex banking problems by implementing advanced techniques like Retrieval Augmented Generation (RAG) and leveraging a stack of open-source and SaaS AI technologies to enhance internal workflows and customer experiences.

Skills

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

What you'll do

Develop and deploy end-user facing AI use cases for the Finance line of business.
Design, test, and support software components including LLM inference, similarity search, and agentic AI.
Fine-tune, develop, and evaluate machine learning models and foundation models.
Construct optimized data pipelines to feed and power machine learning models.
Retrain, maintain, and monitor models in production environments.
Ensure all code follows best practices for responsible, explainable, and risk-governed AI.
Contribute technical vision and leadership to the long-term roadmap of pioneering AI systems.

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 building production-ready data pipelines for ML models.
At least 3 years of experience using industry-recognized ML frameworks like scikit-learn, PyTorch, Dask, Spark, or TensorFlow.
At least 2 years of experience with Retrieval Augmented Generation (RAG) and data preparation for ML models.
Work authorization that require immigration support from an employer.

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