Interested in this AI/ML Engineer role at Moffitt Cancer Center?
Apply Now →About This Role
### Working at Moffitt is both a career and a mission: to contribute to the prevention and cure of cancer.
### As the only National Cancer Institute\-designated Comprehensive Cancer Center based in Florida, Moffitt employs some of the best and brightest minds from around the world. Join a dedicated team of nearly 11,000 who are shaping the future we envision. Moffitt has been recognized as a Best and Brightest Company to Work For in the Nation and is continually named one of the Tampa Bay Times’ Top Workplaces.
Summary
The faculty member will develop and maintain an active research program. The faculty member will support his/her research primarily through extramural grants and publish original research reports in peer\-reviewed scientific journals. The faculty member will recruit and appropriately mentor research personnel within his/her research program. The faculty member will actively and collegially participate in the CCSG programs and the Moffitt Research Institute activities.The Independent Scientist Pathway is intended for tenure\-track individuals who dedicate most of their effort to independent research. A portion of their effort is dedicated to educating future investigators, by teaching graduate students and medical students, and supervising postdoctoral fellows. These individuals will have their primary appointment in a Basic Science or Population Science department. They may have a secondary appointment in a Clinical Science department
Comprehensive Benefit Package \| Relocation Assistance \| Start\-Up Package \& Research Incentive Plan The Department of Machine Learning (ML) at Moffitt Cancer Center, a National Cancer Institute\-designated Comprehensive Cancer Center, is seeking a new faculty member in the tenure\-earning rank of Assistant Member with research interests in artificial intelligence, decision support systems, machine and deep learning, federated learning, and their application in cancer discovery and clinical care. The new faculty will join an expanding ML Department. We currently have faculty initiating a wide range of machine learning research and its application in oncology in collaboration with other members of the Quantitative Science Division, including the Biostatistics and Bioinformatics Department and the Integrated Mathematical Oncology Department, as well as with other research and clinical departments within the cancer center. Moffitt Cancer Center is characterized by a culture of collegiality and team science, facilitating cross\-disciplinary collaborations for cancer research and mentoring. Faculty development is a tenet of Moffitt culture and an essential part of this department’s philosophy to develop future leaders in the emerging field of machine learning in oncology. Position Highlights: • Access to extensive retrospective and prospective data for real\-world predictive analytics, and other clinical research resources, including an integrated repository of clinical, genomic, imaging, and patient\-reported information as well as biospecimens from a large cohort of patients. • Collaboration with implementation scientists offers the opportunity to integrate machine learning algorithms into the electronic medical record and clinic workflows to improve clinical care. • Access to Moffitt’s extensive computational and rich data resources such as the ML Department features state\-of\-the\-art DGX\-A100/DGX\-H100/DGX\-H200 cluster and machine learning engineers for advanced machine learning applications with retrospective and prospective comprehensive clinical datasets, with a focus on data integration and personalized cancer care. The Ideal Candidate: • Expertise in artificial intelligence, decision support systems, machine and, deep learning, federated learning, who are interested in applying this expertise to cancer research and translational oncology. • Preference will be given to applicants with an outstanding record conducting team science or collaborative research with an emphasis on machine learning in healthcare. Areas of interest include: the applications of deep learning, federated learning, explainability and interpretability of machine learning in outcome modeling, human\-machine interaction, clinical decision support, and information retrieval. • Demonstrate experience (or potential) as a collaborative or independent researcher with extramurally funded research studies, presentations at national and international conferences, and a record of high\-quality peer\-reviewed publications. Responsibilities: • Maintain a productive integrated and/or independent research program in machine learning in oncology. • Collaborate on a variety of machine learning research projects both within Moffitt and externally. • Engage in educational (e.g., mentorship) and service activities across Moffitt and its affiliates (AI in cancer with USF). • Contribute to current and initiate future machine learning applications at Moffitt, as evidenced by a history of peer\-reviewed publications and involvement in grant\-supported research projects. Ongoing research includes but is not limited to outcome modeling, human\-machine interaction, clinical decision support, information retrieval, quantitative imaging (radiomics), digital pathology (pathomics), and computational biology, among others, where machine learning can accelerate cancer discovery and improve care delivery. Credentials and Qualifications: • Doctorate degree in computer science, engineering, Physics, mathematics, statistics or a relevant field with appropriate research training and experience. Academic rank beyond Assistant Member will be commensurate with experience and qualifications. Moffitt is affiliated with the University of South Florida, and a University appointment is available in the rank of Assistant/Associate Professor as applicable in the appropriate departments. Moffitt\-based faculty members focus their efforts on research, with minimal expectations for formal teaching. Tampa is a thriving metropolitan city that provides its residents with a high quality of life. The Tampa Bay area has become a hub for groundbreaking research, welcoming individuals from around the globe. This diverse city is engulfed with rich culture, year\-round activities for all, beautiful beaches, amazing cuisine, and so much more. Questions regarding the position should be directed to: Issam El Naqa, PhD, FIEEE, Search Committee Chair, Department of Machine Learning (Issam.Elnaqa@moffitt.org). Salary Range
*Salary ranges posted for this position represent the expected base pay range for the role. Actual compensation may vary based on location and a variety of job\-related factors, including experience, skills, education, and internal equity among Team Members in similar positions.*
*We are committed to maintaining fair and equitable pay practices and regularly review compensation to ensure alignment across our workforce.*
Moffitt Career Site
*If you have the vision, passion, and dedication to contribute to our mission,*
-----------------------------------------------------------------------------------
*then we have a place for you!*
-----------------------------------
1\. Equal Employment Opportunity
Moffitt Cancer Center is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, or protected veteran or disabled status. We seek candidates whose skills, and personal and professional experience, have prepared them to contribute to our commitment to diversity and excellence.
2\. Reasonable Accommodation
Federal law requires employers to provide reasonable accommodation to qualified individuals with disabilities. Please tell us if you require a reasonable accommodation to apply for a job or to perform your job. Examples of reasonable accommodation include making a change to the application process or work procedures, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment. Moffitt endeavors to make moffitt.org/careers accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact one of the Human Resources receptionists by phone at 813\-745\-7899 or by email at HRReceptionists@moffitt.org. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications.
Read more information about your EEO rights under the law.
Transparency in Coverage Rule
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Moffitt Cancer Center, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Moffitt Cancer Center AI Hiring
Moffitt Cancer Center has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Tampa, FL, US, Land O' Lakes, FL, US. Compensation range: $161K - $331K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.