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About This Role
Organization Summary
The University of South Florida (USF) Institute of Applied Engineering (IAE) provides agile, best\-value engineering solutions to enhance the performance and effectiveness of its sponsors, including the Department of Defense, other federal, state, and local agencies, and industry partners.
The Data Science Intern will work under the direction of Accenture Federal Services (AFS) technical leadership to support specialized programs. This internship offers an opportunity to work on meaningful data science and program management initiatives supporting broader Department of Defense (DoD) efforts. The intern will contribute to research, analysis, and the development of tools that enhance operational decision\-making and resource allocation.
The internship program is administered by USF Institute of Applied Engineering. Upon hire, you will be considered both a USF IAE Temporary Employee and an AFS student intern.
Because this internship is partially funded by the U.S. Department of Defense and may involve access to Controlled Unclassified Information (CUI), U.S. citizenship is required for eligibility.
Time Commitment
- Hours of operation are 8:00 AM to 5:00 PM Monday through Friday
- Fall \& Spring Semester: 15–20 hours per week.
- Summer Semester: 20\-29 hours per week.
Internship is primarily in\-person (Tampa, FL) with limited remote work opportunities.
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Opportunities
- Work directly alongside AFS engineers and data scientists.
- Gain hands\-on experience with emerging AI and machine learning technologies.
- Build a professional portfolio of data science and software development projects.
High potential for internship extensions and full\-time employment opportunities following graduation.
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Pay Rate
- $17\.62/hour
Responsibilities
- Support the design, development, and testing of AI\-enabled applications utilizing Large Language Models (LLMs).
- Assist in building and maintaining Retrieval\-Augmented Generation (RAG) pipelines.
- Develop data engineering workflows to collect, clean, transform, and organize structured and unstructured datasets.
- Support integration of AI and machine learning capabilities into cloud\-based environments.
- Collaborate with software engineers and data scientists to evaluate model performance.
- Assist with prompt engineering, model evaluation, and experimentation activities.
- Document technical findings, system architectures, and development processes.
- Contribute to project briefings, demonstrations, and technical presentations.
Qualifications
- Because this internship is partially funded by the U.S. Department of Defense and may involve access to Controlled Unclassified Information (CUI), U.S. citizenship is required for eligibility.
- Pursuing a degree in Computer Science, Data Science, Software Engineering, Computer Engineering, Artificial Intelligence, Mathematics, Statistics, or a related technical discipline.
- Senior standing strongly preferred.
- Minimum GPA of 3\.0
- Ability to report in\-person 2\-3x/week (Tampa, FL)
- Familiarity with machine learning concepts and data science workflows.
- Exposure to Large Language Models (LLMs), generative AI tools, or NLP concepts.
Working at USF
With approximately 16,000 employees, the University of South Florida is one of the largest employers in the Tampa Bay area. We are dedicated to cultivating a talented, engaged and driven workforce that strives to be bold. Employees excel in USF‘s rich academic environment, which fosters their development and advancement. In 2025, Forbes recognized USF as one of Florida’s best large employers, ranked No. 1 among the state’s 12 public universities. Our first\-class benefits package includes medical, dental and life insurance plans, retirement plan options, employee and dependent tuition programs, generous leave, and hundreds of employee perks and discounts.
About USF
The University of South Florida is a top\-ranked research university serving approximately 50,000 students from across the globe at campuses in Tampa, St. Petersburg, Sarasota\-Manatee and USF Health. USF is recognized by U.S. News \& World Report as a top 50 public university and the best value in Florida. U.S. News also ranks the USF Health Morsani College of Medicine as the No. 1 medical school in Florida and in the highest tier nationwide. USF is a member of the Association of American Universities (AAU), a group that includes only the top 3% of universities in the U.S. With an all\-time high of $738 million in research funding in 2024 and as a top 20 public university for producing U.S. patents, USF uses innovation to transform lives and shape a better future. The university generates an annual economic impact of more than $6 billion. USF’s Division I athletics teams compete in the American Athletic Conference. Learn more at www.usf.edu .
Compliance and Federal Notices
*This position may be subject to a Level 1 or Level 2 criminal background check.*
Applicants have rights under Federal Employment Laws :
The University of South Florida is an equal opportunity employer that does not discriminate against any employee or applicant for employment based on any characteristic protected by law. The University maintains programs for protected veterans and individuals with disabilities in accordance with all applicable federal and state laws.
Family and Medical Leave Act (FMLA)
Applicants for USF employment are entitled to request reasonable accommodation(s) in the application process. A request is to be made at least five (5\) working days prior to the time the accommodation(s) is needed. Visit the Central Human Resources ADA Accommodations webpage for more information on requesting an accommodation during the application/interview process.
Equal Employment Opportunity
The University of South Florida is an equal opportunity employer that does not discriminate against any employee or applicant for employment based on any characteristic protected by law. The University maintains affirmative action programs for protected veterans and individuals with disabilities in accordance with all applicable federal and state laws. This job description does not constitute an employment contract.
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 University of South Florida, 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 $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.
University of South Florida AI Hiring
University of South Florida has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tampa, FL, US.
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
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