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About This Role
Job Posting Title:
Postdoctoral Fellow, TEAM\-AI Lab, Department of Quantitative and Systems Health Sciences\-
Hiring Department:
Quantitative and Systems Health Science (QSHS)\-
Position Open To:
All Applicants\-
Weekly Scheduled Hours:
40\-
FLSA Status:
Exempt from FLSA\-
Earliest Start Date:
Immediately\-
Position Duration:
Expected to Continue Until Aug 31, 2027\-
Location:
AUSTIN, TX\-
Job Details:
General Notes
Dell Medical School is seeking a Postdoctoral Fellow, TEAM\-AM Lab for the Department of Quantitative and Systems Health Sciences.
Purpose
The TEAM\-AI Lab seeks multiple Postdoctoral Research Associates to lead methodological innovation, software architecture engineering, and scientific execution across its active grant portfolio. Working under the direct mentorship of Dr. Hongfang Liu and lab faculty, the Postdoctoral Researcher will drive research at the intersection of health data science, multimodal AI, digital twins, computational phenotyping, and responsible AI. PhD must have been received within the last three years
The candidate will hold primary responsibility for designing novel algorithmic frameworks, coordinating multi\-institutional research networks, and translating real\-world health data into actionable clinical intelligence. This position provides structured preparation for an academic tenure\-track career or lead research scientist position in industrial AI labs, providing access to national data networks, high\-performance computing clusters, and clinical interdisciplinary collaborations across UT Austin.
The applicants will join a collaborative research environment at the Translational AI Excellence and Application in Medicine (TEAM\-AI) Lab, focusing on accelerating the translation of AI innovations in biomedicine and healthcare. The lab consists of faculty members, program managers/coordinators, data scientists, and scientific programmers. The activities carried out by the team range from advancing AI innovations through big data, empowering biomedical and clinical sciences through team science collaboration and best practices, to building human\-centered, value\-added, and evidence\-based tools, resources, and services to facilitate real\-world implementation of said innovations.
This position is a temporary with an end date of 08/31/27, renewable based upon availability of funding, work performance, and progress toward goals.
Grant reference
- EMED: An Ethical Mixture\-of\-Experts Digital Twin Framework for Medical Device Surveillance https://reporter.nih.gov/project\-details/11091149
- CardioOnco\-AI: AI\-Empowered Cardiotoxicity Risk Prediction Among Breast Cancer Survivors Using Multi\-Site Real\-World Data: https://www.fda.gov/about\-fda/oncology\-center\-excellence/cardioonco\-ai\-ai\-empowered\-cardiotoxicity\-risk\-prediction\-among\-breast\-cancer\-survivors\-using\-multi
- ReCARDO: Using Real\-World Data to Derive Common Data Elements for Alzheimer's Disease and AD\-Related Dementias Research Through Ontological Innovation: https://reporter.nih.gov/project\-details/11294051
- WONDER: Accelerating Real World Data\-driven Precision Oncology through Data Science and Informatics Excellence in Research: https://cprit.texas.gov/grants\-funded/grants/rr230020
- POI\-KB: Design and Development of a Knowledgebase for Accelerating Perioperative Organ Injury Research and Translation https://reporter.nih.gov/search/mnmQaYO\-z0WKNOW69\_O86g/project\-details/11197028\#description
Responsibilities
- Lead the design and implementation of mixture\-of\-experts neural architectures and reinforcement learning pipelines for counterfactual disease trajectory simulation for EMED, an NIH\-funded multi\-modal AI project.
- Architect and evaluate multi\-site cardiotoxicity risk prediction models integrating structured EHRs, clinical notes via natural language processing, strain echocardiography features, and non\-medical determinants of health under the FDA CardioOnco\-AI award.
- Coordinate AI and computational phenotyping work streams within the national 10\-institution ReCARDO network to extract, standardize, and validate Common Data Elements (CDEs) for Alzheimer's disease research.
- Construct deep language models and clinical natural language processing pipelines to extract structured oncologic phenotypes, molecular biomarkers, and treatment responses from progress notes for the WONDER project.
- Engineer semantic knowledge graphs and database query architectures capturing perioperative pathophysiological mechanisms for acute organ injury research under POI\-KB.
- Authorship of high\-impact first\-author or co\-author manuscripts in leading informatics and machine learning journals and conferences.
- Other related duties as assigned.
EDUCATION \& EXPERIENCE
Minimum Qualifications:
- Ph.D. in Biomedical Informatics, Computer Science, Data Science, Electrical \& Computer Engineering, Applied Mathematics, or a related quantitative field earned within the past three years.
- Strong background in one or more of the following areas:
+ generative and trajectory modeling including transformers, mixture\-of\-experts, reinforcement learning, simulation, and counterfactual analysis.
+ Multimodal NLP \& Fusion, large language models (LLMs), cross\-attention Fusion, vision\-language transformers.
+ Ontological engineering, knowledge graph construction and mining, CDE development for data harmonization.
+ Regulatory science and explainable AI, verification, validation, uncertainty quantification, and AI evaluation framework
- High\-performance computing and big data analytics
- PhD must have been received within the last three years.
Preferred Qualifications:
- Demonstrated understanding of model validation, clinical trial design, and causal inference techniques.
- Experience with transformer\-based models, LLMs, retrieval\-augmented generation (RAG), or foundation models.
- Experience analyzing complex real\-world clinical datasets (e.g., MIMIC\-IV, OMOP CDM, PCORnet, NACC Uniform Data Set, or state cancer registries).
- Knowledge of causal inference, clinical prediction modeling, or multimodal AI.
- Experience with responsible AI, model evaluation, fairness, privacy, or explainable AI.
- Experience with biomedical image processing, radiomics, or digital pathology.
- Scientific programming on Linux or high\-performance computing environments.
LICENSES, REGISTRATIONS OR CERTIFICATIONS
Required:
- None
Preferred:
- None
Salary Range
----------------
$63,480\+ depending on NIH Level
WORKING ENVIRONMENT/EQUIPMENT
- Standard office equipment.
- Repetitive use of a keyboard.
- May be exposed to such occupational hazards as communicable diseases, blood borne pathogens, ionizing and non\-ionizing radiation, hazardous medications and disoriented or combative patients, or others.
- May work in research laboratories, clinical environments, hospitals, ambulatory settings, or field research locations.
- May handle biological specimens, chemicals, hazardous materials, or laboratory equipment consistent with assigned research activities.
- May periodically lift and move research materials and equipment in accordance with organizational safety requirements.
- Requires visual acuity and manual dexterity sufficient to operate research equipment and computer systems.
- May require occasional evening, weekend, or travel commitments for research activities, conferences, and collaborative projects.
Required Materials
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- Resume/CV
- 3 work references with their contact information; at least one reference should be from a supervisor
- Letter of interest
Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi\-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.
Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log\-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.
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Employment Eligibility:
Please make sure you meet all the required qualifications and you can perform all of the essential functions with or without a reasonable accommodation.
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Retirement Plan Eligibility:
The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length. This position has the option to elect the Optional Retirement Program (ORP) instead of TRS, subject to the position being 40 hours per week and at least 135 days in length.
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Background Checks:
A criminal history background check will be required for finalist(s) under consideration for this position.
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Equal Opportunity Employer:
The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.
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Pay Transparency:
The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.
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Employment Eligibility Verification:
If hired, you will be required to complete the federal Employment Eligibility Verification I\-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.
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E\-Verify:
The University of Texas at Austin use E\-Verify to check the work authorization of all new hires effective May 2015\. The university’s company ID number for purposes of E\-Verify is 854197\. For more information about E\-Verify, please see the following:
- E\-Verify Poster (English and Spanish) \[PDF]
- Right to Work Poster (English) \[PDF]
- Right to Work Poster (Spanish) \[PDF]
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Compliance:
Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP\-3031\.
The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2\.206, Austin, Texas 78701\.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At University of Texas at Austin, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
University of Texas at Austin AI Hiring
University of Texas at Austin has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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