Lead Machine Learning Engineer

Capital One Financial

Confirmed live 2 days ago Trusted

Quick summary

Work type: On-site
Location: Cambridge, MAMcLean, VARichmond, VA
Salary: $197,300–$225,100 / yr
Posted: 43 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $225k

This role $211k

$175k most similar roles pay here $273k

This role pays less than 59% of similar roles. Most pay $194,850–$254,750 — the shaded band above. At the midpoint, this role pays about $211k versus about $225k 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 As part of the Risk Tech team, you will collaborate with cross-functional partners to build and deploy proprietary risk management solutions powered by state-of-the-art AI technology. You will design, develop, test, and support AI software components including large language model inference, similarity search, guardrails, and agentic AI. Your daily work involves fine-tuning foundation models, constructing optimized data pipelines, and monitoring models in production to ensure they are well-governed and follow responsible AI practices. To succeed, you must be proficient in Python, Scala, or Java, and possess experience with frameworks like PyTorch, TensorFlow, Scikit-learn, or Spark. You will also utilize tools such as AWS Bedrock, Google Cloud, or Azure. This role focuses on solving complex risk management problems by developing data-driven tools to automatically detect issues before they impact customers or the business.

Skills

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

What you'll do

Design, develop, test, and deploy AI software components including LLM inference, similarity search, and agentic AI.
Fine-tune, evaluate, and maintain machine learning models and foundation models in production environments.
Construct optimized data pipelines to feed and support machine learning models.
Implement guardrails, governance, and observability measures for responsible and explainable AI systems.
Provide technical vision and leadership to shape the long-term roadmap of pioneering AI systems.
Monitor model performance and retrain models to ensure high reliability and accuracy.
Translate complex technical concepts into actionable insights for non-technical partners and stakeholders.

What we're looking for

Bachelor's degree required; Master's or Doctoral degree in a relevant 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 with Retrieval Augmented Generation (RAG) and data preparation for ML models.
Work authorization that require immigration support from an employer.

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