Lead Machine Learning Engineer

Capital One Financial

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Plano, TX
Salary: $179,400–$204,700 / yr
Posted: 66 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $225k

This role $192k

$169k most similar roles pay here $273k

This role pays less than 76% of similar roles. Most pay $195,000–$254,750 — the shaded band above. At the midpoint, this role pays about $192k 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.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Lead Machine Learning Engineer

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As a Lead Machine Learning Engineer, you will join an Agile team dedicated to productionizing Generative AI and advanced agentic systems at scale. You will lead the technical design, development, and implementation of core agentic architectures and multi-agent workflows while ensuring high availability, performance, and security for generative AI applications. Your daily responsibilities include building scalable evaluation and observability frameworks, developing intelligent recommendation engines, and implementing retrieval-augmented generation techniques. To succeed, you must be proficient in Python, Scala, or Java, and possess experience with GenAI frameworks like LangChain, LangGraph, or LlamaIndex, alongside Vector Databases and ML frameworks such as PyTorch, Spark, or TensorFlow. You will solve complex problems regarding model risk, data privacy, and regulatory compliance while providing technical leadership and architectural oversight for engineering teams in a highly regulated environment.

Skills

Generative AI LLM RAG LangChain LangGraph LlamaIndex Python Scala Java PyTorch TensorFlow scikit-learn Spark Dask Vector Databases AWS Azure Google Cloud Platform Distributed Computing

What you'll do

Design and scale core agentic engines and multi-agent workflow solutions for business automation.
Build and integrate scalable evaluation and observability frameworks to ensure model predictability and performance.
Develop production AI solutions including recommendation engines and advanced conversational assistants.
Ensure all AI applications comply with data privacy standards, regulatory requirements, and auditability.
Implement advanced techniques like RAG and LLM optimization into production systems.
Provide technical leadership, architectural oversight, and rigorous code reviews for engineering teams.

What we're looking for

Bachelor's degree in a relevant field.
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.
3+ years of experience with GenAI frameworks like LangChain, LangGraph, LlamaIndex, and Vector Databases.
3 years of experience building, scaling, and optimizing LLM or GenAI orchestration systems in production.
2+ years of experience building automated evaluations and observability pipelines for LLMs.
3+ years of experience with industry-recognized ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow.
Work authorization that require immigration support from an employer.

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