This role pays more than
57%
of similar roles. Most pay
$162,000–$246,150
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$204k
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
878 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 872 roles with salary data.
AI Engineer 4 (AI Foundations, LLM Core and Agentic AI) 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, agentic workflows, similarity search, guardrails, and model evaluation. The engineer will optimize performance metrics like latency, throughput, and cost while managing end-to-end architecture, governance, and observability for complex systems. Key technologies include PyTorch, Huggingface, VectorDBs, AWS Ultraclusters, and programming languages such as Python, Go, Scala, CUDA, C++, C#, and Java. The work focuses on the technical challenge of building scalable, high-performance AI infrastructure and implementing state-of-the-art optimization techniques to ensure reliable, ethical, and secure production systems within a large-scale banking environment.
What does a AI Engineer earn in California?
Median $246150 from 73 postings across 11 companies.
Develop and deploy AI software components including foundation model training, LLM inference, and multi-agent workflows.
Implement state-of-the-art optimization techniques to improve performance, scalability, cost, and latency of production AI systems.
Design end-to-end architectures 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, Electrical Engineering, Computer Engineering, or related fields plus 4 years of experience developing AI/ML algorithms.
Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 2 years of experience developing AI/ML algorithms.
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 optimizing training and inference software to improve hardware utilization, latency, throughput, and cost (preferred).
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Agentic AI
Distributed Systems
C++
Java
Scala
C#
Machine Learning
Model Evaluation
Similarity Search
SaaS
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Agentic AI
Distributed Systems
C++
Java
Scala
C#
Machine Learning
Model Evaluation
Similarity Search
LLM
PyTorch
Huggingface
Python
CUDA
Go
Scala
C++
Java
AWS
VectorDBs
Distributed Systems
Machine Learning
Reinforcement Learning
Multi-agent Workflows
Similarity Search
Model Inference
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Agentic AI
Distributed Systems
C++
Java
Scala
C#
SaaS
Machine Learning
Gen AI
Model Evaluation
Observability
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Agentic AI
Distributed Systems
C++
Java
Scala
C#
SaaS
Machine Learning
Gen AI
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