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About This Role
APPLICATION INSTRUCTIONS:
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- CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply here, apply internally through Workday.
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- CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.
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- If you are NOT a current employee or student, please click “Apply” and complete the application process for external applicants .
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Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants .
POSITION SPECIFICS
Are you a data\-driven marketing leader ready to make a tangible impact at a world\-class institution? The Penn State Smeal College of Business, one of the nation's top\-ranked business schools, has aggressive growth plans and we're looking for a visionary Director of Enrollment Marketing to architect the marketing engine that will drive growth for our portfolio of graduate and undergraduate programs.
This is a hands\-on, performance marketing leadership role for a strategist with deep experience in full\-funnel student recruitment. The ideal candidate is an expert in digital marketing, marketing automation, and campaign optimization, with a passion for leveraging data and technology to achieve ambitious enrollment goals across the college’s entire portfolio. Reporting to the Chief Marketing Officer, this leader will oversee a team of marketing professionals and collaborate across the college to deliver measurable results in student acquisition and conversion.
Key Responsibilities
Strategic Leadership \& Department Management
- Portfolio Strategy: Develop comprehensive, data\-driven marketing strategies across digital, email, social, and traditional channels for a diverse portfolio of academic programs (undergraduate, residential masters, hybrid and online programs, and executive education), optimizing for lead generation, nurturing, and conversion.
- Architect the In\- House Engine: Own and execute email marketing campaigns in Salesforce Marketing Cloud, including segmentation, automated journeys, and integration with Salesforce for lead nurturing and conversion.
- Content Production: Drive content and editorial development for enrollment marketing across all channels, including production management and vendor/resource management as necessary.
- Fiscal Stewardship: Develop, forecast, and manage the enrollment marketing budget; authorize expenditures for paid media, vendor contracts, and technology licenses, ensuring optimal allocation of resources to maximize cost\-per\-enrolled\-student efficiency.
- Innovation : Identify and implement competitive best practices, emerging trends in EdTech, digital advertising, and marketing automation to maintain our program’s advantage.
Team Management \& Development
- Talent Management: Manage a team of marketing professionals (e.g., campaign managers, content specialists, coordinators); define roles and responsibilities to ensure coverage across all degree programs.
- Performance Management: Set clear individual and team goals; conduct performance reviews, provide mentorship, and identify professional development opportunities to foster a high\-performance culture.
- Resource Allocation: Prioritize staff assignments and manage workflow to ensure deadlines are met across multiple simultaneous recruitment cycles.
Data Analytics, ROI \& Performance Measurement
- ROI Analysis: Establish a robust reporting framework to track Key Performance Indicators (KPIs) including Cost Per Lead (CPL), Cost Per Acquisition (CPA), and conversion rates by channel and program.
- Optimization: Utilize analytics to continuously test and optimize campaign performance; present regular executive dashboards to leadership regarding enrollment health and marketing effectiveness.
- Forecasting: Analyze historical data and market trends to predict enrollment outcomes and adjust strategies proactively.
Stakeholder Collaboration \& Admissions Partnership
- Academic Partnership: Serve as the primary marketing liaison to Academic Program Directors and Faculty Chairs; translate their program goals into actionable marketing plans.
- Admissions Integration: Collaborate closely with Admissions leadership to ensure seamless handoffs between marketing\-qualified leads and admissions counselors; align scoring models and communication timing.
- University Alignment: Coordinate with Penn State World Campus and central University Strategic Communications to ensure compliance with university brand standards and leverage enterprise\-wide resources.
Required Qualifications
- Bachelor’s degree in advertising, marketing, business administration, communications, or related field.
- At least 8 years of progressive experience in marketing, with demonstrated success in performance marketing, lead generation, or enrollment marketing
- At least 3 years of supervisory experience, with a proven track record of leading teams and managing direct reports.
- An equivalent combination of education and experience accepted.
- Demonstrated success in developing and executing data\-driven, multi\-channel marketing campaigns that drive measurable enrollment growth.
- Advanced knowledge of CRM systems (Salesforce preferred), email platforms (Salesforce Marketing Cloud preferred), and digital analytics tools.
- Strong oral and written communication skills, with the ability to collaborate and influence across teams.
- Clear leadership abilities, with experience leading and inspiring others.
- Ability to work collaboratively across teams and manage multiple projects in a fast\-paced environment.
- Business instincts, quick learning, and a willingness to challenge conventional wisdom.
- Passion for achieving results and making a measurable impact.
Preferred Qualifications
- Experience in higher education or enrollment marketing.
- Advanced knowledge of digital marketing, paid media, SEO/SEM, and content strategy.
- Experience with marketing dashboard/reporting tools and data visualization.
MINIMUM EDUCATION, WORK EXPERIENCE \& REQUIRED CERTIFICATIONS
Bachelor's Degree\&\#xa;8\+ years of relevant experience, includes 3\+ years of supervisory experience; or an equivalent combination of education and experience accepted\&\#xa;Required Certifications:\&\#xa;None
BACKGROUND CHECKS/CLEARANCES
Employment with the University will require successful completion of background check(s) in accordance with University policies. Due to the financial and fiduciary responsibilities of this position, successful completion of a credit history check will be required in addition to standard background checks.
Penn State does not sponsor or take over sponsorship of a staff employment Visa. Applicants must be authorized to work in the U.S.
SALARY \& BENEFITS
The salary range for this position, including all possible grades, is $86,300\.00 \- $129,500\.00\.
Salary Structure \- Information on Penn State's salary structure
Penn State provides a competitive benefits package for full\-time employees designed to support both personal and professional well\-being. In addition to comprehensive medical, dental, and vision coverage, employees enjoy robust retirement plans and substantial paid time off which includes holidays, vacation and sick time. One of the standout benefits is the generous 75% tuition discount, available to employees as well as eligible spouses and children. For more detailed information, please visit our Benefits Page .
CAMPUS SECURITY CRIME STATISTICS
Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here .
EEO IS THE LAW
Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814\-865\-1473\.
Penn State is committed to and accountable for advancing equity, respect, and belonging. We embrace individual uniqueness, as well as a culture of belonging that supports equity initiatives, leverages the educational and institutional benefits of inclusion in society, and provides opportunities for engagement intended to help all members of the community thrive. We value belonging as a core strength and an essential element of the university’s teaching, research, and service mission.
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Salary Context
This $86K-$129K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Penn State 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($107K) sits 35% below the category median. Disclosed range: $86K to $129K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Penn State University AI Hiring
Penn State University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in University Park, PA, US. Compensation range: $129K - $129K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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