below marketmost similar roles pay hereabove market
This role pays more than
88%
of similar roles. Most pay
$192,050–$211,200
— the blue band above.
At the midpoint, this role pays about
$211k
versus about
$202k
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.
The AI Engineer 4 joins the Intelligent Foundations and Experiences team to build and deploy proprietary AI solutions central to the business. You will partner with cross-functional teams to design, develop, test, 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 invent optimization techniques to improve performance, scalability, cost, and latency for large-scale production systems while owning end-to-end architecture for maintainability and ethical alignment. The role requires proficiency in Python, Go, Scala, CUDA, or Java, alongside experience with AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch. You will solve complex technical problems involving GPU/TPU utilization, model performance drift, and data governance to deliver reliable, high-performance AI infrastructure.
What does a AI Engineer earn in Massachusetts?
Median $246150 from 61 postings across 4 companies.
Design, develop, test, deploy, and support AI software components including foundation model training and LLM inference.
Build and maintain agents, multi-agent workflows, similarity search, guardrails, and model evaluation systems.
Implement state-of-the-art optimization techniques to improve scalability, cost, latency, and throughput of production AI systems.
Own the end-to-end architecture for complex AI systems to ensure maintainability, observability, and ethical alignment.
Define and maintain service-level objectives for AI reliability, including latency, 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 new AI deployments meet security, data governance, and compliance standards.
Mentor senior associates on scalable design, performance tuning, and translating research into production environments.
What we're looking for
Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related field plus 4 years of experience developing AI and ML algorithms.
Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related field 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
Distributed Systems
Model Evaluation
Observability
GPU
TPU
Python
Go
Scala
CUDA
Java
C++
C#
PyTorch
AWS
Huggingface
VectorDBs
LLM Inference
Distributed Systems
Similarity Search
Model Evaluation
Observability
Data Governance
Python
Go
Scala
CUDA
Java
C++
C#
PyTorch
Huggingface
AWS
Google Cloud
Azure
VectorDBs
LLM Inference
Distributed Systems
Model Evaluation
Observability
GPU
TPU