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About This Role
University of St. Thomas Vice President for Enrollment Management
The Vice President for Enrollment Management (VPEM) serves as the University of St. Thomas' senior leader for enrollment strategy and retention. Reporting directly to the President and serving as a member of the President's Council, the VPEM provides strategic leadership for Admissions, Financial Aid, and the Registrar, ensuring a coherent, mission-aligned enrollment enterprise that supports the University's academic priorities and long-term sustainability.
The VPEM will lead the development and execution of an integrated enrollment strategy that reflects UST's Catholic mission and commitment to forming students for lives that flourish and professions that serve. This leader will approach enrollment as a mission-critical institutional system that integrates recruitment, financial aid strategy, and communications in service of students and responsible stewardship of institutional resources, while partnering with academic leadership to support student retention.
In collaboration with Marketing and campus stakeholders, the VPEM will oversee the development of enrollment strategies that are supported by effective, coordinated communications across recruitment, yield, and retention. These efforts will clearly articulate the value of a UST education to prospective and current students and families, with particular attention to personal engagement consistent with the University's mission across all student populations.
The VPEM will provide strategic leadership for both undergraduate and graduate enrollment, recognizing the distinct roles these populations play in the University's academic vitality and financial health. This includes stabilizing and strengthening undergraduate enrollment and student quality, increasing the number of undergraduate students recruited from outside of the Houston area, and advancing thoughtful, sustainable growth in graduate and professional programs aligned with institutional strengths and market demand.
In collaboration with the Vice President for Finance, the Vice President for Enrollment Management will contribute to multi-year tuition and enrollment projections and support sound financial planning. The VPEM will also leverage data, analytics, and emerging technologies to inform decision-making, improve operational effectiveness, and enhance the scalability and sophistication of enrollment systems and processes. At the same time, this leader will balance data-informed strategy with creativity, judgment, and a deep understanding of the human dimensions of enrollment work, including the cultivation of strong, trust-based relationships with secondary schools, academic partners, and other external stakeholders.
Finally, the VPEM will lead, mentor, and develop a talented enrollment staff, fostering a culture of collaboration, accountability, professional growth, and shared purpose. The successful candidate will be a visible, trusted partner across the University community, working closely with faculty, staff, campus ministry, and students to advance UST's mission and enrollment success.
VPEM PRIMARY RESPONSIBILITIES
Leadership
- Engage and galvanize the university community around a shared mission-aligned vision for enrollment and adapting to the highly competitive enrollment landscape; lead the campus community in understanding current trends and enacting creative solutions to combat arising challenges.
- Provide executive leadership and oversight for Admissions, Financial Aid, and the Registrar, ensuring coherent policies, processes, and service across the student lifecycle.
- Serve as a member of the President's Council and a close strategic partner to the President, providing regular updates and presentations to senior leadership over university-specific enrollment goals and performance measures.
- Ensure high-quality, responsive, and mission-consistent first impressions for prospective students and families, working through enrollment management leadership and in partnership with academic stakeholders across all points of engagement.
- Partner with academic leadership to advance student retention and lend expertise on the residential housing campaign.
- Guide undergraduate enrollment strategy to stabilize and strengthen enrollment, student quality, and mission fit, while advancing graduate and professional enrollment through thoughtful growth aligned with institutional strengths, market demand, and program quality.
- Ensure enrollment strategies are supported by effective communications and decision-support insights, leveraging data, analytics, and technology to inform day-to-day planning and continuously improve recruitment, yield, and retention outcomes, in collaboration with campus stakeholders.
- Build, develop, and sustain high-performing teams across Admissions, Financial Aid, and the Registrar, fostering a culture of excellence, collaboration, accountability, and continuous professional development, while strengthening recruitment capacity and student-facing engagement in support of responsible, mission-aligned enrollment strategy.
- Represent UST values while participating in both internal and external committees, professional organizations, events, programs, and panels.
Finances
- Align enrollment strategies with revenue goals, optimize financial aid modeling, and support long-term financial planning through accurate enrollment forecasting and tuition revenue projections.
- Maintain budget and allocate resources appropriately to achieve institutional goals, can manage and balance multiple budgets at once.
Enrollment Marketing
- Develop engaging recruitment and yield campaigns to enhance the reputation of the university and increase reach among talented students nationally and internationally.
- Foster and maintain strong relationships with college counselors and other stakeholders in key markets and feeder high schools that are critical to recruitment efforts.
Additional Skills
- Outstanding communication skills and storytelling, including the ability to persuasively convey academic distinctions and community differentiators to prospective students, families, and college counselors within the marketplace.
Required:
- Bachelor's degree and a minimum of 10 years of enrollment management experience or a closely related area within higher education, with responsibility for institution-wide strategy and cross-functional leadership.
- Demonstrated experience leading integrated enrollment strategies that span recruitment, financial aid, and student persistence/retention outcomes.
- Proven ability to collaborate effectively with senior leaders across academics, finance, marketing, and student-facing units to advance institution-wide priorities.
- Advanced competency using data, analytics, and performance indicators to inform strategy, evaluate results, and drive continuous improvement.
- Experience leading, mentoring, and developing high-performing teams, including a track record of building healthy organizational culture with clear goals and accountability.
- Understanding of the mission of a Catholic university, including respect for the religious and intellectual traditions articulated in Ex Corde Ecclesiae.
Preferred:
- Master's degree and 10 years of enrollment management experience at a college or university setting, with at least 5 years of experience in a senior-level enrollment leadership position.
- Demonstrated understanding of Catholic doctrine and the mission of Catholic higher education, and the ability to articulate and advance the University's Catholic mission and identity in an inviting and faithful manner.
- Experience leading or significantly advancing graduate and professional enrollment strategies within a comprehensive university or similar institutional context.
- Demonstrated experience serving as a visible, external-facing institutional leader, including building trust-based relationships with schools, academic partners, or other key constituencies.
*The University of St. Thomas is committed to the religious, ethical, and intellectual traditions of Catholic higher education. As permitted by law, practicing Catholics who will advance the mission of the school are preferred for this position. However, the University invites all qualified applicants to apply.*
The University of St. Thomas is an equal opportunity employer.
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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At University of St. Thomas Houston, 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 Required
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 $154,000 based on 8,743 positions with disclosed compensation.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
University of St. Thomas Houston AI Hiring
University of St. Thomas Houston has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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