Lead Machine Learning Engineer, Finance Tech - AI Enablement

Capital One Financial

Confirmed live today Low trust

Quick summary

Work type: On-site
Location: Cambridge, MANew York, NYMcLean, VA
Salary: $197,300–$225,100 / yr
Employment: Full-time
Posted: 105 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $229k

This role $211k

$174k $289k

below market most similar roles pay here above market

This role pays less than 65% of similar roles. Most pay $202,800–$254,750 — the blue band above. At the midpoint, this role pays about $211k versus about $229k 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 2107 open roles on FindRole.

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

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Lead Machine Learning Engineer, Finance Tech - AI Enablement

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The 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. You will partner with cross-functional teams to design, develop, test, and support AI software components, including large language model inference, similarity search, guardrails, and agentic AI. Key responsibilities include fine-tuning foundation models, constructing optimized data pipelines, and monitoring production models while ensuring Responsible and Explainable AI standards. You will utilize a broad stack of Open Source and SaaS technologies, including Python, Scala, Java, PyTorch, Dask, Spark, and TensorFlow. This role solves complex business problems by building scalable AI infrastructure and production-ready systems to deliver transformative AI capabilities within the finance line of business.

Skills

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

What you'll do

Design, develop, test, and deploy AI software components including LLM inference and similarity search.
Fine-tune, develop, and evaluate machine learning and foundation models for production use.
Construct and optimize data pipelines to feed machine learning models.
Retrain, maintain, and monitor models in production environments.
Implement AI guardrails, governance, observability, and Responsible AI best practices.
Provide technical vision and thought leadership for the long-term AI systems roadmap.
Manage code quality to reduce vulnerabilities and ensure model governance from a risk perspective.

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).
7+ years of experience designing, developing, delivering, and supporting AI services at scale (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).

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