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About This Role
Job Title: Director of Financial Aid
Reports to: VP of Student Experience and Success
Location: Salem University / Hybrid
Classification: Full-Time, Exempt
POSITION SUMMARY:
The Director of Financial Aid provides strategic leadership, oversight, and accountability for all financial aid operations at Salem University, with primary responsibility for Title IV regulatory compliance, audit readiness, and institutional risk management. This role serves as the University’s subject-matter expert on federal and state financial aid regulations, leads all financial aid compliance audits and corrective action plans, and ensures the accuracy, timeliness, and integrity of financial aid processing, including work performed by third-party servicers.
The Director partners closely with enrollment, academics, student services, finance, and external regulators to support student access, affordability, retention, and graduation while safeguarding the institution’s compliance standing.
PRIMARY RESPONSIBILITIES:
Regulatory Compliance & Audit Leadership
· Serve as the institutional lead for Title IV, DOE, and state financial aid compliance, ensuring adherence to all applicable laws, regulations, and guidance.
· Lead all financial aid audits, program reviews, and compliance assessments, including coordination with external auditors, consultants, and the Department of Education.
· Develop, implement, and monitor Corrective Action Plans (CAPs) and ongoing compliance improvement initiatives.
· Maintain institutional policies, procedures, and internal controls aligned with regulatory expectations and audit standards.
· Proactively identify compliance risks and implement mitigation strategies.
Financial Aid Operations & Oversight
· Direct all financial aid services for new and continuing students across online and on-site modalities.
· Ensure accurate and timely awarding, disbursement, reconciliation, and reporting of financial aid funds.
· Oversee complex compliance functions including, but not limited to, R2T4, verification, Pell recalculations, enrollment reporting (NSLDS), COD reporting, and reconciliations.
· Ensure the accuracy, clarity, and compliance of all student-facing financial aid communications.
Third-Party Processor Oversight
· Provide direct oversight and accountability for third-party financial aid processing partners, ensuring services are delivered accurately, timely, and in full compliance with federal regulations and contractual requirements.
· Establish performance metrics, reporting cadences, and quality assurance reviews to monitor processor outcomes.
· Serve as the primary institutional escalation point for processing discrepancies, compliance concerns, and corrective actions.
Systems, Data & Reporting
· Leverage student information systems and reporting tools to monitor compliance, operational performance, and student outcomes.
· Utilize data analytics to inform decision-making, identify trends, and support continuous improvement.
· Maintain strong working knowledge of Student Information Systems (Anthology Student experience preferred) and related federal systems (COD, NSLDS, FSA platforms).
Leadership & Collaboration
· Recruit, hire, train, and evaluate financial aid staff, fostering a culture of compliance, accountability, and student-centered service.
· Provide leadership, coaching, and professional development to build institutional regulatory capacity.
· Collaborate with enrollment, registrar, finance, academic leadership, and student support services to align financial aid practices with broader student success initiatives.
POSITION REQUIREMENTS:
· Master’s degree in higher education administration, business, or a related field required; bachelor’s degree with extensive, directly relevant experience may be considered.
· Minimum of 5–7 years of progressive financial aid administration experience, with substantial responsibility for Title IV compliance and audit management.
· Demonstrated expertise in DOE regulations, Title IV programs, and federal/state compliance frameworks.
· Proven experience leading or supporting financial aid audits, program reviews, and corrective action plans.
· Experience overseeing or working with third-party financial aid processors or servicers.
· Strong proficiency with student information systems and federal aid platforms (Anthology Student preferred).
· Excellent analytical, organizational, communication, and problem-solving skills.
· Demonstrated commitment to student access, affordability, integrity, and continuous improvement.
How to Apply:
Please submit a cover letter, resume, and contact information for three professional references to:
Office of the President
Salem University
OfficeofthePresident@salemu.edu
EEO Statement:
As an EEO/AA employer, Salem University and its components will not discriminate in our employment practices based on an applicant’s race, ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information or status as a protected veteran.
Job Type: Full-time
Pay: $75,000.00 - $85,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Vision insurance
Work Location: Remote
Salary Context
This $75K-$85K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 33,423 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Salem 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600. This role's midpoint ($80K) sits 48% below the category median. Disclosed range: $75K to $85K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Salem University AI Hiring
Salem University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $85K - $85K.
Remote Work Context
Remote AI roles pay a median of $160,000 across 1,226 positions. About 7% of all AI roles offer remote work.
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 33,423 open positions tracked in our dataset. By seniority: 3,283 entry-level, 20,769 mid-level, 6,381 senior, and 2,990 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,320 positions). The remaining 30,984 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 33,423 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (30,275), AI Software Engineer (749), AI Product Manager (741). 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 (3,283) are outnumbered by mid-level (20,769) and senior (6,381) 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 2,990 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,320 positions), with 30,984 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (21,235 postings), Aws (11,126 postings), Rust (9,803 postings), Python (4,999 postings), Azure (3,220 postings), Gcp (2,707 postings), Prompt Engineering (1,817 postings), Openai (1,487 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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