AI Ops Research Engineer-Mid -Level

USAA•San Antonio, TX

1d•$93,770 - $168,790•Onsite

About The Position

USAA is seeking an AI Operations Research Engineer to conduct applied research and experimentation to identify and advance emerging AI capabilities for Application and Infrastructure Operations. The role monitors industry trends, AI/ML research, open-source technologies, and vendor capabilities, and translates promising developments into prototypes and evidence-based recommendations. This hands-on role explores machine learning, generative AI, agentic AI, predictive operations, intelligent observability, and automation to determine which capabilities can improve reliability, resilience, incident management, and operational efficiency. Success is measured by turning emerging technology into validated opportunities that can inform product and engineering investment. The position is based in San Antonio, Texas, and has a flexible working environment with an on-site requirement of five days per week. Relocation assistance is not available. USAA does not provide visa sponsorship for this role.

Requirements

Bachelor's degree in a STEM or Business field with applied quantitative or behavioral research fields, or four years of relevant education and/or experience.
At least four 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.
Experience designing experiments, developing prototypes, analyzing results, and making evidence-based recommendations.
Working knowledge of machine learning, statistical analysis, generative AI, and LLM concepts.
Ability to rapidly evaluate emerging technologies and distinguish technical potential from production readiness.
Strong written and verbal communication skills, including communicating complex findings to technical and nontechnical audiences.
Knowledge of AI governance, model risk, data management, privacy, security, or operational risk principles.

Nice To Haves

Experience applying AI/ML to AIOps, observability, reliability engineering, infrastructure operations, or incident management.
Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
Experience researching or prototyping generative AI, agentic AI, or autonomous operations capabilities.
Experience building AI evaluation frameworks, including model comparisons, benchmarks, regression testing, and failure-mode analysis.
Familiarity with AIOps and observability technologies such as Dynatrace, BigPanda, Moogsoft, OpenTelemetry, Prometheus, or Grafana.
Experience evaluating emerging AI technology in a regulated or highly controlled environment.
Demonstrated success transitioning research or prototypes into engineering roadmaps or production-development initiatives.

Responsibilities

Research emerging AI, AIOps, observability, automation, and industry trends to identify technologies and approaches with potential value for Application and Infrastructure Operations.
Translate operational challenges and emerging technologies into research hypotheses, experiments, prototypes, and measurable evaluation criteria.
Evaluate machine learning, generative AI, agentic AI, predictive analytics, anomaly detection, root-cause analysis, and intelligent automation for operational use cases.
Develop rigorous evaluation and testing methods to assess accuracy, reliability, scalability, failure modes, production readiness, and business value.
Translate validated research into technical recommendations and production opportunities, identifying which capabilities should advance, be refined, or be discontinued.
Communicate research findings and recommendations to engineering, product, risk, and leadership stakeholders, including key opportunities, limitations, risks, and investment considerations.