Lead AI Engineer

Capital One Financial

Quick summary

Work type
On-site
Location
San Jose, CA · McLean, VA · Cambridge, MA · New York, NY
Salary
$197,300–$225,100 / yr
Posted
99 days ago

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $217k
This role $211k
$170k most similar roles pay here $270k

This role pays less than 52% of similar roles. Most pay $184,206–$249,753 — the shaded band above. At the midpoint, this role pays about $211k versus about $217k 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 498 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 495 roles with salary data.

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View all roles at Capital One Financial

At a glance

TL;DR · Lead AI Engineer

As a Lead AI Engineer at our cutting-edge technology firm, you will join a dynamic and innovative team focused on advancing the capabilities of large-scale production AI systems. Your primary responsibilities include leveraging a diverse stack of open-source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, and PyTorch to develop state-of-the-art LLM optimization techniques that enhance performance metrics like scalability, cost efficiency, latency, and throughput. You will work on complex challenges in natural language processing and machine learning, contributing to the development of robust AI solutions for enterprise clients. Ideal candidates possess extensive experience with Python, PyTorch, and cloud-based AI services, along with a strong background in machine learning research and production system optimization.

What you'll do

  • Lead the implementation of advanced AI technologies like AWS Ultraclusters and Huggingface.
  • Develop state-of-the-art techniques to optimize large language models for production systems.
  • Enhance scalability, cost efficiency, latency, and throughput in AI system performance.
  • Utilize VectorDBs and Nemo Guardrails to improve data management and security.
  • Work with PyTorch and other frameworks to build robust machine learning solutions.

What we're looking for

  • Extensive experience with AWS Ultraclusters and other SaaS AI technologies.
  • Proficient in using Huggingface, VectorDBs, Nemo Guardrails, and PyTorch.
  • Expertise in optimizing large-scale production AI systems for performance.
  • Strong background in inventing state-of-the-art LLM optimization techniques.
  • Deep technical knowledge and hands-on experience with AI technologies.

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