Interested in this AI/ML Engineer role at Virginia Commonwealth University?
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- 45002610
- 907 Floyd Avenue, MCV Main Campus, Virginia, United States, 23284
- SVP Health Sciences SML
- Research
- Restricted Full-Time
- Virginia Commonwealth University
Advertising Summary: At VCU, we take pride in our deep commitment to our postdoctoral community. In 2026, the National Postdoctoral Association (NPA) named VCU as one of only 12 recipients of its inaugural Institutional Policy Awards, in recognition of our transformative efforts to elevate quality of life and workplace equity for postdocs.
VCU postdocs are Virginia state employees, with benefits mirroring 12-month faculty. We adhere closely to the NPA’s guidelines for best practices in postdoc training. We work closely with our local NPA chapter, the VCU Postdoc Association, to make this a welcoming and productive place to complete your training!
Unit: School Of Medicine MBU
Department: Physical Medicine and Rehabilitation
Department Summary:
Background:
We are recruiting postdoctoral scholars for the NIH-funded T32 Postdoctoral Training Program in Translational Population Health and Data Science Using Service Member, Veteran and Civilian Healthcare Datasets.
Virginia Commonwealth University (VCU) and the Central Virginia VA Health Care System have partnered to create a unique big data analytics post-doctoral training program that includes didactic and experiential learning and training in use of large Veteran as well as civilian datasets. The program includes development and implementation of advanced analytics methods, not limited to machine learning methods and Artificial Intelligence.
As the commonwealth’s capital, Richmond, Virginia, offers a vibrant mix of history, culture and real-world opportunities. Distinct neighborhoods, cozy restaurants and cafes and quaint local markets give the city an intimate feel, but theaters, galleries, music festivals, sports attractions and a steady stream of annual events bring RVA — as locals call the region — to life.
Description of the Opportunity:
This T32 program will prepare postdoctoral fellows with the skills and competencies in Big Data Analytics (BDA) methods and techniques to support Evidence-Based Medicine (EBM). The datasets that will be available to the new T32 fellows will be among the largest in U.S. health care. The VA Corporate Data Warehouse holds detailed health care records on approximately 25 million veterans spanning the past 25 years, with roughly 4 billion additional data points added each day. Centers for Medicare and Medicaid Services datasets, housed at VCU through the Center for Rehabilitation Science and Engineering Data Hub, cover more than 65 million Americans enrolled annually in Medicare programs. The training program is co-led by Virginia Commonwealth University and Central Virginia Veterans Affairs Health Care System. The overarching objective of this 5-year T32 training program is to recruit and train a skilled cohort of postdoctoral fellows (MD, DO, PhD, ScD) who will develop competencies and build capacity in real-world evidence translation for service members, Veterans and civilians using population health and data science approaches.
Potential novel areas of focus include:
- Health services utilization and outcomes for beneficiaries on Medicare and Medicaid programs
- Assessing health of service members transitioning from DoD to VA care
- Exploring healthcare utilization and outcomes of dually-enrolled (CMS/VA) Veterans
- Harmonizing CMS-VA-DOW datasets to understand the complex needs and outcomes of these populations
- Comparing risk and protective factors for healthcare utilization and outcomes between civilians and military veterans
- Informing civilian population and individual-level interventions, research programs, and policy
- Examining quality of services across military, veteran and civilian healthcare systems
The primary appointment for the fellows will be in the Department of Physical Medicine and Rehabilitation (PM&R) in School of Medicine at VCU which is one of the most well-established PM&R departments in the country and has received international recognition for neurorehabilitation, including stroke, brain and spinal cord trauma research. The 70+ VCU PM&R faculty, including physicians and psychologists, provide clinical, research and teaching across VCU Health, SAI, Children’s Hospital of Richmond and Central Virginia Veterans Affairs Health Care System. The Department also has 7 post-graduate fellowship training programs in TBI/Polytrauma, Amputation, SCI, Pain Management, Integrative Medicine, Sports Medicine and Rehabilitation Research.
The position is affiliated with VCU’s Center for Rehabilitation Science and Engineering (CERSE) and the Wright Regional Center for Clinical and Translational Science. VCU and CERSE provide a rich training environment including a commitment to creating a supportive mentoring environment, didactic and hands-on research training, a broad local and national mentorship network, large datasets for analyses, statistical support, and individualized professional development opportunities.
Program Activities:
Our program will recruit fellows from both PhD/ScD and MD/DO tracks. Each fellow will participate in two years of training activities. Upon training program completion, each fellow is expected to:
- understand responsible conduct of research with use of population health datasets, claims, survey data, and data from Electronic Health Records (EHR),
- develop grant writing skills needed to be competitive for external grant funding,
- understand the fundamentals of translational population health and data science research studies in terms of study design, dataset identification, data management and analysis, and dissemination of results,
- perform hypothesis-driven translational research by developing a multi-disciplinary team,
- disseminate study findings via scientific peer-reviewed publications/presentations, and
- complete coursework and training in the design and conduct of rigorous, controlled research studies.
As appropriate for individuals’ career goals, proposed activities mentioned above and research experience will be supplemented by a series of additional activities designed to intensify and enhance the abilities of trainees to conduct translational population health and Data Science-related research studies using service member, Veteran and civilian healthcare datasets. Our program’s didactic options include: (a) recommended formal coursework as applicable; (b) formal master’s degree program as applicable; (c) seminar and workshops; and (d) grant writing institute and workshops.
The primary fellowship goals are to develop a publication portfolio in the fellow’s line of research, work towards seeking a research faculty or research position, and prepare preliminary materials for a research career development or other grant application that the fellow can use in their subsequent role.
Mentoring Team:
Fellows will also work with a combination of mentors from different health domains and those with methodological expertise from the VCU schools of Medicine, Nursing, Dentistry, Pharmacy, and Public Health; the College of Engineering; the College of Health Professions; and Central Virginia Veterans Affairs Health Care System. Together, program mentors hold $20 million in active annual research funding. The complete list of available mentors is on the CERSE website at https://pmr.vcu.edu/research/data-hub/.
Program Timeline and Goals:
The T32 BDA Fellowship is a two-year fellowship. Applicants will be reviewed as they are received until slots are filled.
Eligibility:
Applicants must complete all PhD graduation requirements including dissertation defense prior to their start date. T32 scholars must adhere to the NIH NRSA eligibility requirements, which require candidates to be US citizens or permanent residents.
Required Qualifications/Experience:
Requires completion of a PhD/ScD or MD/DO training
Knowledge of population health, big data analytics or a health domain that could benefit from big data research
Proficiency in developing, navigating and maintaining datasets
Evidence of excellent initiation, interpersonal and organizational skills
Evidence of excellent communication skills, both oral and written
Publication history in health or big data analytics
Interest and commitment to improving the lives of Veterans as well as civilians
Evidence of working well as part of a research project or team
Demonstrated experience working in and fostering a supportive faculty, staff, and student environment and a commitment to do so as a Post-Doctoral Fellow at VCU.
Position Details:
As determined by the T32 budget, fellows will be hired as a postdoctoral research fellow with an entry level postdoctoral fellow salary according to the current National Research Service Awards (NRSA) stipend levels https://grants.nih.gov/grants/guide/notice-files/NOT-OD-26-044.html. A successful candidate for the position is expected to reside in the Richmond, VA area. Hybrid-remote and flexible work arrangements are available if they do not interfere with the candidate’s research and training.
Application Requirements:
- Curriculum Vitae (CV)
- Statement of interest with applicant’s career goals, research accomplishments, experiences in practice/research, attributes and experiences that would provide value to training and research, and how participating in the VCU BDA T32 program would advance their career goals
- Transcript(s)
- Copies of sample publications and grants authored
- Two letters of recommendation
Benefits: All VCU employee types are eligible for a wide array of benefits to support you during your employment at VCU. Consult the benefits website for information on benefits eligibility according to employee type.
Salary Range: $63,480
FLSA Exemption Status: Exempt
ORP Eligible: Yes
Restricted Position: This is a restricted position with no set end date; continued employment is dependent upon project need, availability of funding, and performance.
Flexible Work Arrangement: Hybrid
Contact Information:
Contact Name: Joshua Hahn
Contact Email: [email protected]
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 Virginia Commonwealth 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.
Virginia Commonwealth University AI Hiring
Virginia Commonwealth University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in VA, 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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