below marketmost similar roles pay hereabove market
This role pays more than
72%
of similar roles. Most pay
$192,050–$225,600
— the blue band above.
At the midpoint, this role pays about
$211k
versus about
$209k
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
2229 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 1126 roles with salary data.
The AI Engineer 4 joins the Intelligent Foundations and Experiences team to develop responsible and reliable AI systems for banking. You will partner with cross-functional teams to design, develop, test, deploy, and support AI software components, including foundation model training, large language model inference, agents, multi-agent workflows, similarity search, guardrails, and model evaluation. You will own end-to-end architecture for complex systems, ensuring maintainability, observability, and ethical alignment while optimizing GPU/TPU utilization. The role requires proficiency in Python, Go, Scala, CUDA, or Java, alongside experience with PyTorch, Huggingface, VectorDBs, and AWS Ultraclusters. You will solve technical problems related to scalability, cost, latency, and throughput for production AI systems, translating scientific research into production-ready solutions that enhance customer interactions and internal workflows.
What does a AI Engineer earn in New York?
Median $245800 from 70 postings across 9 companies.
Design, develop, test, and deploy AI software components including foundation model training and LLM inference.
Build and support agents, multi-agent workflows, similarity search, and model guardrails.
Implement 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 with infrastructure engineering teams.
Lead cross-functional technical reviews to ensure AI deployments meet security, governance, and compliance standards.
Mentor senior associates on scalable design, performance tuning, and research-to-production translation.
What we're looking for
Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 4 years of experience developing AI and ML algorithms.
Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 2 years of experience developing AI and ML algorithms.
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 using Python, C++, C#, Java, CUDA, or Golang (preferred).
Experience developing and applying state-of-the-art techniques for optimizing training and inference software (preferred).
Python
Go
Scala
CUDA
Java
C++
C#
PyTorch
Huggingface
AWS
Google Cloud
Azure
VectorDBs
LLM Inference
Agentic AI
Distributed Systems
Model Evaluation
Observability
Python
Go
Scala
CUDA
Java
C++
C#
PyTorch
AWS
Kubernetes
Huggingface
VectorDBs
LLM Inference
Agentic AI
Distributed Systems
Model Evaluation
Observability
Python
Go
Scala
CUDA
Java
C++
C#
PyTorch
Huggingface
AWS
Google Cloud
Azure
VectorDBs
LLM Inference
Agentic Workflows
Distributed Systems
Model Evaluation
Observability
Python
Go
Scala
CUDA
Java
C++
C#
PyTorch
Huggingface
AWS
Google Cloud
Azure
VectorDBs
LLM Inference
Agentic AI
Distributed Systems
Model Evaluation
Observability