Interested in this AI/ML Engineer role at The University of Texas at San Antonio?
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Location: San Antonio, TX
Regular/Temporary: Regular
Job ID: 15729
Full/Part Time: Full Time
Position Information
The University of Texas at San Antonio (UT San Antonio) is a nationally recognized, top\-tier public research university that unites the power of higher education, biomedical discovery and healthcare within one visionary institution. As the third\-largest research university in Texas and a Carnegie R1\-designated institution, UT San Antonio is a model of access and excellence \- advancing knowledge, social mobility and public health across South Texas and beyond. UT San Antonio serves approximately 42,000 students in 320 academic programs spanning science, engineering, medicine, health, liberal arts, AI, cybersecurity, business, education and more. With 17,000 faculty and staff, UT San Antonio has also been recognized as a Top Employer in Texas by Forbes Magazine. Learn more online, on UT San Antonio Today or on X, Instagram, Facebook, YouTube or LinkedIn.
Salary Range: Up to $47,000/Annualized, commensurate with education, experience, and qualifications.
Job Type: Full Time
Posting Close Date: Applications will be accepted through 11:59 PM CDT on 7/24/2026\. At the discretion of the hiring department, this posting may close once a sufficient number of qualified applications have been received.
Required Application Materials:
- Resume is required.
- Cover letter is required.
Job Details
Job Summary
The Senior Administrative Associate provides high\-level support to staff and faculty, ensuring efficient office operations and exercising independent judgment. This role includes a variety of administrative, financial, and coordination tasks. The Senior Administrative Associate ensures that daily operations directly contribute to the department's goals and objectives.
Core Responsibilities
+ Manages administrative activities to ensure daily operations within the department or college run efficiently. Identifies areas for improvement in the workflow and processes and provides recommendations to increase efficiency.
+ Provides support for human resources\-related tasks involving recruitment and onboarding, and ensures a smooth integration process for new employees. Adheres to deadlines set forth by university stakeholders.
+ Communicates clearly and ensures all team members are kept informed by relaying messages, updates, and important information.
+ Supports departmental/college projects by assisting management with timelines, tracking progress, and ensuring projects align with the main goals.
+ Serves as a lead and coordinates the work of other administrative and support staff. Provides guidance, ensures duties are performed correctly, and assists with administrative issues as they arise.
+ Supports budget preparation by gathering and compiling information for budget planning. Organizes financial records and data sets for budget reporting.
+ Maintains and updates departmental calendars, meeting schedules, and appointments. Communicates any changes promptly to avoid conflicts or confusion.
+ Provides administrative support, including drafting correspondence, preparing reports, and performing quality control, such as proofreading and editing documents.
+ Performs other duties as assigned.
Required Qualifications
+ High School Diploma, Vocational Training, or Apprenticeships in a related field.
+ Five (5\) years of related work experience.
+ This position requires the ability to maintain the security and integrity of UT San Antonio and its infrastructure per Texas EO\-GA\-48\.
Knowledge, Skills, and Abilities
+ Microsoft Office Suite
+ Documentation \& Records Management
+ Data Entry
+ Call Management
+ Calendar Management
+ Customer Service Management
+ Content Design \& Development
+ Database Management
+ Business Process Improvement
+ Active Listening
Working Conditions
+ Office environment. Workdays, areas, and work hours may vary based on departmental needs.
Physical Demands
+ Sedentary work; sitting most of the time. Jobs are sedentary; if walking and standing are required, only occasionally. Ability to exert up to 10 pounds of force to lift, carry, push, pull, or otherwise move objects.
This position will work primarily on campus. Travel and parking expenses are the employee's responsibility.
This position is contingent upon a successful background check. Verification of a valid driver's license and Motor Vehicle Record (MVR) may be completed as applicable. Applicants selected must be able to show proof of eligibility to work in the United States by time of hire.
Equal Employment Opportunity
As an equal employment opportunity and affirmative action employer, it is the policy of The University of Texas at San Antonio to promote and ensure equal employment opportunity for all individuals regardless of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability or genetic information, and veteran status. The University is committed to the Affirmative Action Program in compliance with all government requirements to ensure nondiscrimination.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At The University of Texas at San Antonio, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
The University of Texas at San Antonio AI Hiring
The University of Texas at San Antonio has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Antonio, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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