below marketmost similar roles pay hereabove market
This role pays less than
65%
of similar roles. Most pay
$126,800–$214,000
— the blue band above.
At the midpoint, this role pays about
$131k
versus about
$170k
for comparable roles.
Based on 240 similar postings.
Employer
About USAA
USAA (United Services Automobile Association) is a San Antonio-based Fortune 500 financial services company founded in 1922, dedicated to providing insurance, banking, and investment solutions exclusively to U.S. military members, veterans, and their families.
USAA currently has
73 open roles
on FindRole.
Listed pay typically runs
$114,080–$218,030
across 44 roles with salary data.
The AI Ops Research Engineer-Mid -Level conducts applied research and experimentation to advance emerging AI capabilities for Application and Infrastructure Operations. Working within this specialized team, the role involves monitoring industry trends and open-source technologies to translate promising developments into prototypes and evidence-based recommendations. Key responsibilities include evaluating machine learning, generative AI, agentic AI, predictive analytics, and intelligent automation to improve reliability, incident management, and operational efficiency. The successful candidate will develop rigorous testing methods to assess accuracy, scalability, and production readiness. Required skills include Python, statistical analysis, and experience with ML frameworks like PyTorch, TensorFlow, or scikit-learn. The role also requires familiarity with observability tools such as Dynatrace, Prometheus, and Grafana to solve complex operational challenges in a regulated environment.
Research emerging AI, AIOps, and automation trends to identify technologies for Application and Infrastructure Operations.
Translate operational challenges into research hypotheses, experiments, prototypes, and measurable evaluation criteria.
Evaluate machine learning, generative AI, and agentic AI for use cases like anomaly detection and root-cause analysis.
Develop rigorous testing methods to assess accuracy, reliability, scalability, and production readiness of new technologies.
Convert validated research into technical recommendations and production opportunities for engineering and product teams.
Communicate research findings, risks, and investment considerations to technical and non-technical stakeholders.
What we're looking for
Bachelor's degree in a STEM or Business field with applied quantitative or behavioral research fields; OR 4 years of relevant education and/or experience.
4+ years of experience in applied research, AI/ML, data science, software engineering, operations research, or related technical fields.
Strong programming and analytical skills using Python and relevant AI/ML or data-analysis tools.
Working knowledge of machine learning, statistical analysis, generative AI, and LLM concepts.
Knowledge of AI governance, model risk, data management, privacy, security, or operational risk principles.
Strong written and verbal communication skills to communicate complex findings to technical and nontechnical audiences.
Experience applying AI/ML to AIOps, observability, reliability engineering, infrastructure operations, or incident management (preferred).
Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn (preferred).
Python
TensorFlow
PyTorch
Deep Learning
Natural Language Processing
Computer Vision
AWS
Azure
GCP
Machine Learning
Data Pipelines
Model Serving
Cloud Computing
Software Development Methodologies