AI Engineer 4, Gen AI Platform Services

Capital One Financial

Confirmed live today High trust

Quick summary

Work type
On-site
Location
San Francisco, CAMcLean, VACambridge, MASan Jose, CANew York, NY
Salary
$197,300–$225,100 / yr
Employment
Full-time
Posted
5 days ago
Freshness
Confirmed live today

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $211k
This role $211k
$187k $235k
below market most similar roles pay here above market

This role pays more than 70% of similar roles. Most pay $192,050–$230,400 — the blue band above. At the midpoint, this role pays about $211k versus about $211k 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 1917 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1045 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · AI Engineer 4, Gen AI Platform Services

The AI Engineer 4 joins the Intelligent Foundations and Experiences team to develop responsible and reliable AI systems for banking. You will design, develop, test, deploy, and support AI software components, including foundation model training, large language model inference, agents, multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. You will own end-to-end architecture for complex systems, ensuring maintainability and ethical alignment while optimizing GPU/TPU utilization. The role requires proficiency in Python, Go, Scala, CUDA, or Java, alongside experience with AWS Ultraclusters, Huggingface, and VectorDBs. You will solve technical problems related to scalability, cost, latency, and throughput for production AI systems. Key responsibilities include mentoring associates, defining service-level objectives for model performance drift, and translating scientific research into production-ready software.

What does a AI Engineer earn in California?

Median $246150 from 84 postings across 12 companies.

See salary data

What you'll do

  • Design, develop, test, and deploy AI software components including LLM inference, agents, and multi-agent workflows.
  • Implement similarity search, guardrails, model evaluation, and observability for production AI systems.
  • Develop foundation model optimization techniques to improve scalability, cost, latency, and throughput.
  • Own the end-to-end architecture for complex AI systems to ensure maintainability and ethical alignment.
  • Define and maintain service-level objectives for AI reliability, including uptime and model performance drift.
  • Optimize GPU/TPU utilization and accelerate model inference pipelines in collaboration with infrastructure engineering.
  • Lead cross-functional technical reviews to ensure AI deployments meet security, data governance, and compliance standards.
  • Mentor senior associates on scalable design, performance tuning, and translating research into production.

What we're looking for

  • Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 4 years of AI/ML experience.
  • Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 2 years of AI/ML experience.
  • At least 4 years of experience programming with Python, Go, Scala, CUDA, or Java.
  • Experience leading development of AI systems with tradeoff decisions around cost, latency, throughput, and accuracy (preferred).
  • 6+ years of experience deploying scalable and responsible AI solutions on cloud platforms (preferred).
  • Experience designing, developing, delivering, and supporting AI services (preferred).
  • Experience developing AI and ML algorithms like LLM Inference, Similarity Search, and VectorDBs using Python, C++, C#, Java, CUDA, or Golang (preferred).
  • Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization (preferred).

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