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Description
The Texas A&M University College of Performance, Visualization & Fine Arts (PVFA) and the Virtual Production Institute (VPI) invite applications for a full-time, 9-month tenure-track faculty position in Artificial Intelligence (AI) and Animation at the rank of Assistant Professor within the Technical Arts and Interactive Media section. The anticipated start date is August 1, 2027.
Candidates should demonstrate strong potential for scholarly achievement and the ability to establish a nationally and internationally recognized research and/or creative works agenda in AI and Animation. We welcome applicants working broadly across animation, computer graphics, artificial intelligence, character animation, human motion, and related areas.
We are particularly interested in candidates whose research and/or creative works can advance performance capture and animation, broadly understood to include the capture, analysis, synthesis, modeling, and generation of expressive human and character movement. Relevant approaches may include motion capture, markerless and multimodal capture, AI-driven and generative motion models, computational performance, expressive character animation, real-time animation, and related emerging techniques.
Areas of expertise may include, but are not limited to:
- Animation craft and aesthetics, including performance, acting, timing, and character in animated movement
- Character animation and advanced animation techniques
- AI-driven animation, generative motion models, and neural representations
- Human motion analysis, synthesis, and computational performance
- Performance and motion capture in complex and real-time environments
- Markerless and multimodal capture approaches
- Expressive character movement and digital performance
- Applications in media and entertainment, virtual production, game-engine-based character performance, simulation and training, and other interdisciplinary domains
The successful candidate will be expected to establish an active program of research and/or creative works, pursue external funding opportunities, disseminate work through publications, presentations, exhibitions, software systems, patents, or other recognized scholarly outcomes, and contribute to the national and international visibility of PVFA and the VPI.
The successful candidate will also be expected to teach courses at the undergraduate and graduate levels, advise and mentor graduate students, participate in all aspects of PVFA's and the VPI's activities, and serve the professional community.
About the College
The College of Performance, Visualization & Fine Arts was formed in 2022 at Texas A&M University, bringing together the Department of Visualization, the Department of Performance Studies, and the Dance Science Program. PVFA is built without departments. Faculty sections and academic programs sit at the college level, which places musicians, composers, actors, directors, choreographers, dancers, painters, cinematographers, technical artists, level designers, spatial audio engineers, tools programmers, ethnomusicologists, art historians, UI/UX designers, and virtual set designers in one structure rather than in separate units. The College places a heavy emphasis on collaboration and interdisciplinary work in both scholarly and creative research.
The College offers the BS, MS, and MFA in Visualization, a PhD in Visual Computing and Interactive Media, and degree programs in music, dance, and theatre. It operates at the main campus in Bryan-College Station and as part of Texas A&M – Fort Worth, with facilities equipped for work at the intersection of the arts and emerging technology. Applicants should consult the college's website (https://pvfa.tamu.edu/) to review our academic and research programs.
About the Virtual Production Institute This position is aligned with the mission of the Virtual Production Institute. Formed in 2023, the VPI is a technology-focused ecosystem for teaching, learning, and research. It develops emerging applications and the skilled workforce needed for virtual reality, augmented reality, sensors, projectors, and computing across multiple disciplines and industries. Its facilities in Bryan-College Station and Fort Worth include a performance capture and sound stage with supporting technology studio classrooms. Applicants should consult the institute's website (https://pvfa.tamu.edu/institutes/vpi/).
About the Ph.D. in Visual Computing and Interactive Media (VCIM)
The Ph.D. in Visual Computing and Interactive Media (VCIM) provides an interdisciplinary environment for advanced research at the intersection of computing, visualization, interactive media, and the arts. The program brings together faculty and students working across areas such as computer graphics, animation, artificial intelligence, visualization, virtual and augmented reality, interactive systems, and related creative and computational disciplines.
The successful candidate will have opportunities to advise VCIM doctoral students, develop graduate courses related to their research expertise, and collaborate across the College and the University on interdisciplinary research and creative projects. The successful candidate is expected to participate actively in the VCIM doctoral program, including advising Ph.D. students and, when eligible, chairing dissertation committees.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Texas A&M University, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Texas A&M University AI Hiring
Texas A&M University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in College Station, TX, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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