This role pays more than
64%
of similar roles. Most pay
$192,050–$235,750
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$214k
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
936 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 933 roles with salary data.
AI Engineer 4 (Vision model (VLM) customization experience) joins the Intelligent Foundations and Experiences team to develop and deploy proprietary AI solutions that enhance products for millions of customers. This role involves designing, testing, and supporting software components including foundation model training, large language model inference, multi-agent workflows, similarity search, guardrails, and model evaluation. The engineer will implement state-of-the-art optimization techniques to improve performance metrics like latency, throughput, and cost for production systems while managing end-to-end architecture and service-level objectives. The technical stack includes PyTorch, Huggingface, VectorDBs, AWS Ultraclusters, and programming languages such as Python, Go, Scala, CUDA, C++, C#, and Java. The work focuses on the core challenge of building scalable, responsible AI infrastructure to solve complex problems in the banking sector through advanced machine learning engineering and research-to-production translation.
What does a AI Engineer earn in New York?
Median $246150 from 69 postings across 9 companies.
Design, develop, test, and deploy AI software components including foundation model training and LLM inference.
Implement agentic workflows, similarity search, guardrails, and model evaluation systems for production use.
Develop optimization techniques to improve performance, scalability, cost, latency, and throughput of large-scale AI systems.
Manage end-to-end architecture for complex AI systems ensuring maintainability, observability, and ethical alignment.
Define and maintain service-level objectives (SLOs) for AI reliability including uptime and model performance drift.
Collaborate with infrastructure teams to optimize GPU/TPU utilization and accelerate inference pipelines.
Lead technical reviews to ensure new 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, Engineering, or related fields with 4 years of experience developing AI/ML algorithms, or a Master's degree with 2 years of experience.
At least 4 years of experience programming with Python, Go, Scala, CUDA, or Java.
Experience leading development of AI systems with trade-offs regarding 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/ML algorithms such as LLM inference, similarity search, vector databases, and guardrails using various programming languages (preferred).
Experience applying state-of-the-art techniques to optimize training and inference software for hardware utilization and performance (preferred).
Experience building agentic AI systems and workflows (preferred).
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Distributed Systems
C++
Java
Scala
C#
Machine Learning
Multi-agent Workflows
Similarity Search
Model Evaluation
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Distributed Systems
C++
Java
Scala
C#
Machine Learning
Multi-agent Workflows
Similarity Search
Model Evaluation
Observability
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Distributed Systems
C++
Java
Scala
C#
Machine Learning
Multi-agent Workflows
Similarity Search
Model Evaluation
SaaS
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Distributed Systems
C++
Java
Scala
C#
Machine Learning
Agentic Workflows
Similarity Search
Model Evaluation
Observability
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Distributed Systems
C++
Java
Scala
C#
Machine Learning
Multi-agent Workflows
Similarity Search
Model Evaluation
Observability
LLM
PyTorch
Huggingface
Python
CUDA
Go
Scala
C++
Java
AWS
VectorDBs
Distributed Systems
Machine Learning
Reinforcement Learning
Multi-agent Workflows
Similarity Search
Model Inference