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About This Role
University Overview
The University of Pennsylvania, the largest private employer in Philadelphia, is a world\-renowned leader in education, research, and innovation. This historic, Ivy League school consistently ranks among the top 10 universities in the annual U.S. News \& World Report survey. Penn has 12 highly\-regarded schools that provide opportunities for undergraduate, graduate and continuing education, all influenced by Penn’s distinctive interdisciplinary approach to scholarship and learning. As an employer Penn has been ranked nationally on many occasions with the most recent award from Forbes who named Penn one of America’s Best Large Employers in 2023\.
Penn offers a unique working environment within the city of Philadelphia. The University is situated on a beautiful urban campus, with easy access to a range of educational, cultural, and recreational activities. With its historical significance and landmarks, lively cultural offerings, and wide variety of atmospheres, Philadelphia is the perfect place to call home for work and play.
The University offers a competitive benefits package that includes excellent healthcare and tuition benefits for employees and their families, generous retirement benefits, a wide variety of professional development opportunities, supportive work and family benefits, a wealth of health and wellness programs and resources, and much more.
Posted Job Title
Creative Technologist, Wharton Generative AI LabsJob Profile Title
Application Developer SeniorJob Description Summary
Founded in 1881 as the world’s first collegiate business school, the Wharton School of the University of Pennsylvania is shaping the future of business by incubating ideas, driving insights, and creating leaders who change the world. With campuses in both Philadelphia and San Francisco, Wharton has over 850 staff, a faculty population of more than 235 renowned professors, and 5,000 undergraduate, MBA, executive MBA, and doctoral students. Each year 13,000 professionals from around the world advance their careers through Wharton Executive Education’s individual, company\-customized, and online programs. More than 104,000 Wharton alumni form a powerful global network of leaders who transform business every day. Wharton is home to a dynamic community of staff, bringing a wide range of skills, experiences, and perspectives. To learn more, visit www.wharton.upenn.edu.
The Wharton Generative AI Labs blends academic rigor with hands\-on experience in educational technology to provide valuable insights into AI’s potential for enhancing how we teach, learn, and work. We create solutions that bring together pedagogical principles and technological innovation, and produce rigorous scientific research that advances our understanding of large language models through systematic testing and analysis.Job Description
Position Summary
The Creative Technologist designs and builds AI\-enabled tools, systems, and experiences that advance GAIL's mission to catalyze responsible and innovative uses of AI. Working within a fast\-moving and highly creative team, the Creative Technologist treats generative AI as a creative medium, one that calls for judgment, craft, and original thinking, and turns that approach into working resources that faculty, students, and external audiences can use, learn from, and build on. The role combines creative and intellectual ambition with strong technical ability, including fluency with agentic coding tools such as Claude Code and Codex and the design and use of agents and multi\-agentic systems. It is well suited to an entrepreneurial, self\-motivated builder who is excited to create with AI, who sees new opportunities and acts on them, and who sustains their creativity and relevance as generative AI tools continue to advance.
The ideal individual is entrepreneurial, self\-directed, and thrives in a fast\-moving environment. They are able to identify opportunities, define problems, establish direction, and move initiatives forward with confidence. They demonstrate creative ambition, bringing originality, sound judgment, and a high standard of quality to their work.
This position reports to the Executive Director of GAIL and requires a minimum of two days on campus per week.
This is a full\-time, limited\-term position that is contingent upon funding and continued business needs. The position is for up to two (2\) years, with the possibility of extension, subject to review on business needs and continued funding.
To apply, please submit (1\) A cover letter, (2\) a resume, and (3\) a portfolio that provides examples of 1\-3 projects done with generative AI. Please include a brief explanation of each project's goals, how it was built, and how any AI\-related challenges were addressed. Prompt histories in PDF form and GitHub links are appreciated. Applicants who do not submit all three of the above requirments will not be considered.
Job Responsibilities
- Build: Design and build AI\-enabled tools, systems, agents, and multi\-agentic systems that advance GAIL's mission, using agentic coding tools such as Claude Code and Codex.
- Direct the web presence: Lead the creative direction and ongoing development of GAIL's web presence, working with operations staff who handle routine updates.
- Evaluate: Evaluate and compare AI tools and models, and apply sound judgment about which emerging capabilities to build on.
- Teach and facilitate: Support AI\-enabled learning across GAIL by leading build sessions, facilitating technical work, and helping faculty and students make effective use of AI.
- Pursue new opportunities: Initiate self\-directed projects, identifying new opportunities and bringing them to working form.
- Document: Document technical decisions, standards, and design choices so GAIL can scale its work and retain what it learns.
- Stay current: Stay current as generative AI advances, and bring new capabilities into GAIL's work.
- Lead/Manage: Lead individuals who have a technical scope of work. Oversee work as it relates to the technical function of the lab.
- Collaborate: Collaborate closely with GAIL faculty and staff, helping to shape the work as well as carry it out.
Qualifications
Required:
- Bachelor’s degree and 3 to 5 years of experience working in an environment of technological innovation, applied creativity, or equivalent combination of education and experience.
- A portfolio of generative AI work that demonstrates functional tools, systems, or experiences you have built. We evaluate demonstrated ability rather than years of experience; what matters is what you have made and the judgment behind it.
- Strong technical ability to build and ship, including fluency with generative AI tools, agentic coding tools such as Claude Code and Codex, and the design and use of agents and multi\-agentic systems.
- Clear evidence of creative ambition, with work that shows originality and a high standard for quality.
- Demonstrated ability to adapt and remain current as generative AI tools advance.
- Strong communication skills, with the ability to explain technical and creative ideas to both technical and nontechnical audiences.
- Comfort working in an academic environment and a commitment to responsible AI use.
Preferred:
- Teaching, instruction, or workshop facilitation experience. The ability to help others learn to build and create with AI is a strong asset in this role.
- A background in a creative field such as design, art, film, music, writing, or architecture, combined with strong building ability. We welcome candidates who came to AI from a creative discipline.
- Experience shaping a digital or web presence, with the design sensibility to keep it current and engaging.
- Experience in higher education, research centers, innovation labs, or design studios.
- Experience building agents, multi\-agentic systems, interactive prototypes, or AI\-enabled learning experiences.
Job Location \- City, State
Philadelphia, PennsylvaniaDepartment / School
Wharton SchoolPay Range
$84,961\.00 \- $105,000\.00 Annual Rate
Salary offers are made based on the candidate’s qualifications, experience, skills, and education as they directly relate to the requirements of the position, and in alignment with salary ranges based on external market data for the job’s level. Internal organization and peer data at Penn are also considered.
Equal Opportunity Statement
The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state or local law.
Special Requirements
Background checks may be required after a conditional job offer is made. Consideration of the background check will be tailored to the requirements of the job.
University Benefits
- Health, Life, and Flexible Spending Accounts: Penn offers comprehensive medical, prescription, behavioral health, dental, vision, and life insurance benefits to protect you and your family’s health and welfare. You can also use flexible spending accounts to pay for eligible health care and dependent care expenses with pre\-tax dollars.
- Tuition: Take advantage of Penn's exceptional tuition benefits. You, your spouse, and your dependent children can get tuition assistance here at Penn. Your dependent children are also eligible for tuition assistance at other institutions.
- Retirement: Penn offers generous retirement plans to help you save for your future. Penn’s Basic, Matching, and Supplemental retirement plans allow you to save for retirement on a pre\-tax or Roth basis. Choose from a wide variety of investment options through TIAA and Vanguard.
- Time Away from Work: Penn provides you with a substantial amount of time away from work during the course of the year. This allows you to relax, take vacations, attend to personal affairs, recover from illness or injury, spend time with family—whatever your personal needs may be.
- Long\-Term Care Insurance: In partnership with Genworth Financial, Penn offers faculty and staff (and your eligible family members) long\-term care insurance to help you cover some of the costs of long\-term care services received at home, in the community or in a nursing facility. If you apply when you’re newly hired, you won’t have to provide proof of good health or be subject to underwriting requirements. Eligible family members must always provide proof of good health and are subject to underwriting.
- Wellness and Work\-life Resources: Penn is committed to supporting our faculty and staff as they balance the competing demands of work and personal life. That’s why we offer a wide variety of programs and resources to help you care for your health, your family, and your work\-life balance.
- Professional and Personal Development: Penn provides an array of resources to help you advance yourself personally and professionally.
- University Resources: As a member of the Penn community, you have access to a wide range of University resources as well as cultural and recreational activities. Take advantage of the University’s libraries and athletic facilities, or visit our arboretum and art galleries. There’s always something going on at Penn, whether it’s a new exhibit at the Penn Museum, the latest music or theater presentation at the Annenberg Center, or the Penn Relays at Franklin Field to name just a few examples. As a member of the Penn community, you’re right in the middle of the excitement—and you and your family can enjoy many of these activities for free.
- Discounts and Special Services: From arts and entertainment to transportation and mortgages, you'll find great deals for University faculty and staff. Not only do Penn arts and cultural centers and museums offer free and discounted admission and memberships to faculty and staff. You can also enjoy substantial savings on other goods and services such as new cars from Ford and General Motors, cellular phone service plans, movie tickets, and admission to theme parks.
- Flexible Work Hours: Flexible work options offer creative approaches for completing work while promoting balance between work and personal commitments. These approaches involve use of non\-traditional work hours, locations, and/or job structures.
- Penn Home Ownership Services: Penn offers a forgivable loan for eligible employees interested in buying a home or currently residing in West Philadelphia, which can be used for closing costs or home improvements.
- Adoption Assistance: Penn will reimburse eligible employees on qualified expenses in connection with the legal adoption of an eligible child, such as travel or court fees, for up to two adoptions in your household.
*To learn more, please visit:* *https://www.hr.upenn.edu/PennHR/benefits\-pay*
Salary Context
This $84K-$105K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 Pennsylvania, 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. This role's midpoint ($94K) sits 56% below the category median. Disclosed range: $84K to $105K.
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 Pennsylvania AI Hiring
University of Pennsylvania has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Philadelphia, PA, US. Compensation range: $74K - $105K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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