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About This Role
- 45002433
- 907 Floyd Avenue, MCV Main Campus, Virginia, United States, 23284
- SVP Health Sciences SML
- Faculty
- Faculty (Salaried)
- Virginia Commonwealth University
Advertising Summary: Virginia Commonwealth University (VCU) School of Medicine is a premier academic medical center located in the heart of Richmond. Accounting for almost half of VCU’s sponsored research, the School of Medicine is internationally recognized for patient care and education. Virginia Commonwealth University is an equal opportunity employer.
Unit: School of Medicine
Department: Cellular, Molecular and Genetic Medicine
Department Summary: The Department of Cellular, Molecular and Genetic Medicine is dedicated to unraveling the fundamental biology that supports health and drives disease. Our faculty, trainees and students tackle some of the most pressing challenges in biomedical science, from the molecular and genetic roots of disease to the complexities of whole-body physiology.
Our foundational studies are driving the frontiers of scientific discovery, pushing the boundaries of what we understand about biology, disease and the human body. From our labs in downtown Richmond to research collaborations around the world, our work is generating new knowledge, influencing the direction of biological science and redefining what is possible in biomedical research.
We are proud to be a community of passionate educators and mentors, deeply committed to empowering the next generation of scientists and leaders. With alumni enjoying successful careers across a broad spectrum of industries, our rigorous training programs offer students a transformative education that drives innovation today and creates lasting impact for tomorrow.
Mission:
The Department of Cellular, Molecular and Genetic Medicine at Virginia Commonwealth University School of Medicine is committed to advancing molecular discovery, disease biology, and translational medicine through innovative research, education, and collaboration. The Division of Biomedical AI within CMGM is being developed to integrate artificial intelligence, machine learning, computational methods, and biomedical data infrastructure with molecular discovery and cancer research.
This position will support highly collaborative research across CMGM, Massey Comprehensive Cancer Center, the School of Medicine, and other VCU research programs. The goal is to strengthen collaborative cancer research through advanced spatial multi-omics analysis, scalable data systems, reproducible computational pipelines, AI-enabled research workflows, and cloud-based biomedical data infrastructure.
Chief Purpose of this Position:
The Department of Cellular, Molecular and Genetic Medicine seeks a full-time, 12-month, non-tenure-track Research Assistant Professor in Biomedical AI, Spatial Multi-Omics, and Cancer Data Science. This is a 100% research position focused on collaborative and team-based biomedical research, with particular emphasis on supporting cancer center projects and translational research programs.
The candidate will bring expertise in spatial multi-omics, database management, data curation and provenance, reproducible pipeline development, AI agent development, and cloud computing. The candidate will work closely with investigators across CMGM, Massey Comprehensive Cancer Center, and the broader School of Medicine to develop, implement, and maintain computational infrastructure for high-impact biomedical and cancer research.
Duties & Responsibilities:
Research: 100%
Contribute to collaborative biomedical and cancer research projects through the development and application of computational methods, data infrastructure, and AI-enabled research workflows.
Responsibilities include developing and maintaining reproducible pipelines for spatial transcriptomics, spatial proteomics, single-cell multi-omics, imaging, and related biomedical data types; designing data management systems that support data curation, metadata tracking, provenance, quality control, and reproducibility; supporting database development and integration for large-scale cancer and molecular medicine datasets; and implementing cloud-based computing workflows for scalable analysis and collaboration.
The candidate will also contribute to AI-enabled research infrastructure, including AI agents for data analysis, workflow automation, documentation, quality control, and reproducible biomedical discovery. The successful candidate will collaborate with faculty, clinicians, trainees, and research staff; contribute to manuscripts, grant applications, presentations, and reports; and help ensure that computational workflows are rigorous, transparent, scalable, and reusable across collaborative research programs.
Qualifications:
Minimum Qualifications
- PhD or equivalent degree in bioinformatics, computational biology, biomedical data science, computer science, biostatistics, statistics, engineering, genomics, cancer biology, or a related field
- Demonstrated expertise in computational analysis of biomedical data, with experience in one or more areas such as spatial omics, single-cell analysis, multi-omics integration, cancer data science, biomedical AI, or translational bioinformatics
- Experience developing reproducible computational pipelines using programming languages such as Python, R, Nextflow, Snakemake, or related tools
- Experience with database management, data organization, metadata standards, data curation, quality control, and/or data provenance
- Demonstrated ability to work effectively in highly collaborative, multidisciplinary biomedical research teams
- Strong communication skills and ability to work with basic scientists, clinicians, computational scientists, trainees, and research staff
- Demonstrated ability to work in and foster an environment of respect, professionalism, and civility with a population of faculty, staff, and students from all backgrounds and experiences, or a commitment to do so as a faculty member at VCU
Preferred Qualifications
The candidate will have expertise in one or more of the following areas:
- Spatial transcriptomics, spatial proteomics, imaging-based spatial biology, single-cell genomics, or multimodal spatial multi-omics
- Cancer data science, tumor microenvironment analysis, cancer ecosystem modeling, immune-tumor interaction analysis, therapeutic response studies, or translational cancer research
- Development of scalable and reproducible pipelines for high-dimensional biomedical data analysis
- Database design, biomedical data models, metadata frameworks, data provenance, data harmonization, and long-term data stewardship
- Cloud computing and high-performance computing environments, including platforms such as Google Cloud, AWS, Azure, or institutional HPC systems
- AI agent development, workflow automation, LLM-enabled research tools, automated data analysis systems, or AI-assisted documentation and quality control
- Software engineering practices, version control, containerization, workflow management, API development, and collaborative code development
- Experience contributing to collaborative grant applications, manuscripts, cancer center research programs, shared resources, or team science initiatives
Application Materials:
Applicants should submit a curriculum vitae, a brief research and technical expertise statement, a collaborative research statement, and names of references. Review of applications will begin immediately and continue until the position is filled.
A competitive startup and research support package will be provided to help the successful candidate develop collaborative biomedical AI, spatial multi-omics, and cancer data science infrastructure.
Salary Range: Commensurate
Benefits: All full-time university staff are eligible for VCU's robust benefits package, that includes comprehensive health benefits, paid annual and holiday leave, generous tuition benefits, retirement planning and savings options, tax-deferred annuity and cash match programs, employee discounts, well-being resources, abundant opportunities for career development and advancement, and more.
FLSA Exemption Status: Exempt
Sensitive Position: No
Restricted Position: No
ORP Eligible: Yes
Flexible Work Arrangement: Other
Rank: Assistant Professor
Tenure: Ineligible
Months: 12 months
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 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 $212,000 based on 11,866 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 $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Virginia Commonwealth University AI Hiring
Virginia Commonwealth University has 3 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer. Based in VA, US. Compensation range: $90K - $150K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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