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About This Role
##### Higher Education AI Enrollment Growth \& Institutional Partnerships Executive
##### Company: HireNow Staffing (Direct Placement Partner)
##### HireNow Snapshot
##### HireNow Staffing is actively recruiting a seasoned Higher Education AI Enrollment Growth \& Institutional Partnerships Executive to join one of our valued client partners. This opportunity is built for a high\-performing sales professional who combines direct college or university admissions/enrollment experience with proven Account Executive closing success.
##### The selected candidate will own new institutional business across higher education, managing the sales process from qualified opportunity through discovery, demonstration, proposal, negotiation, and signed agreement. With SDR support generating leads, this individual will focus on converting institutional opportunities while developing trusted relationships with admissions, enrollment, and recruitment leaders.
##### This is a relationship\-driven sales environment with a fast sales cycle, meaningful influence over go\-to\-market execution, and an opportunity to help expand access to AI\-enabled college counseling.
##### Key Responsibilities
- Own the full new\-business sales cycle for higher education institutional accounts.
- Convert SDR\-generated opportunities through discovery, product demonstrations, proposals, negotiations, and contract execution.
- Build relationships with admissions, enrollment management, recruitment, and other institutional decision\-makers.
- Conduct consultative discovery to understand enrollment objectives, operational challenges, and institutional priorities.
- Translate institutional needs into compelling business cases combining measurable outcomes with mission\-driven value.
- Manage multiple stakeholders throughout purchasing and approval processes.
- Negotiate pricing and partnership terms while establishing relationships positioned for long\-term retention.
- Maintain disciplined pipeline management, forecasting, opportunity documentation, and CRM activity.
- Collaborate with leadership and internal teams to refine messaging, sales processes, and go\-to\-market execution.
- Represent the organization at higher education conferences, meetings, and industry events requiring approximately 25% travel.
##### Required Qualifications
- 3\+ years of relevant professional experience.
- Direct professional experience working in admissions and/or enrollment within a college or university is required.
- Proven Account Executive or comparable new\-business sales experience with demonstrated closing results.
- Documented ability to communicate measurable sales performance, such as ARR generated, quota attainment, institutional accounts closed, or revenue growth.
- Experience selling to senior decision\-makers and navigating multi\-stakeholder buying processes.
- Strong consultative selling skills with the ability to connect institutional objectives to product value.
- Excellent written, verbal, presentation, negotiation, and relationship\-building abilities.
- Ability to communicate effectively using both data\-driven business cases and mission\-centered storytelling.
- Comfortable operating within a small, rapidly evolving organization where individual ownership is significant.
- Ability to travel approximately 25%.
- Ability to work within the required hybrid schedule at the New York City headquarters.
##### Preferred Qualifications
- Account Executive experience within a Seed or Series A startup.
- Familiarity with artificial intelligence, education technology, college\-access technology, or higher education SaaS.
- Established professional relationships across college and university admissions or enrollment organizations.
- HubSpot or comparable CRM proficiency.
- Experience in partnership\-oriented roles combining relationship development with revenue ownership.
- Demonstrated success closing higher education institutional agreements.
- Experience selling solutions with approximately $20K–$50K institutional contract values.
- Stable employment history demonstrating increasing sales responsibility and measurable performance.
##### HireNow Package
##### Compensation: $90,000 base salary \+ $90,000 variable compensation for $180,000 OTE at target performance.
##### Commission: 50/50 compensation structure with uncapped commission. Commission structure is based on the provided plan; final quota and payout mechanics should be confirmed during the interview process.
##### Equity: Meaningful early\-stage equity opportunity.
##### Employment Type: Full\-Time \| Direct Placement
##### Work Location: Hybrid – New York City, New York headquarters.
##### Travel: Approximately 25%.
##### Visa Sponsorship: H\-1B, O\-1, and OPT support available.
##### HireNow Checklist
##### HireNow Staffing is recruiting a Higher Education AI Enrollment Growth \& Institutional Partnerships Executive who:
- Brings direct college or university admissions/enrollment experience—not experience limited to adjacent academic functions.
- Has transitioned that institutional knowledge into measurable Account Executive or new\-business sales success.
- Can provide concrete evidence of closed revenue, ARR growth, quota performance, or comparable sales results.
- Understands how admissions and enrollment leaders evaluate institutional priorities and purchasing decisions.
- Can independently move opportunities from discovery and demonstration through proposal, negotiation, and close.
- Builds credibility with senior institutional decision\-makers through consultative, relationship\-centered selling.
- Is comfortable working in an early\-stage environment with significant ownership and GTM influence.
##### HireNow Standout Candidates
##### Candidates will receive the strongest consideration if they demonstrate:
- A rare combination of hands\-on higher education admissions/enrollment experience and proven closing experience.
- Quantifiable success generating new ARR or exceeding sales targets within an early\-stage technology organization.
- Existing relationships with admissions, enrollment management, or university recruitment leaders.
- Experience selling AI, EdTech, SaaS, or technology solutions into higher education.
- Success navigating fast sales cycles while protecting relationship quality and long\-term account value.
- Strong command of HubSpot or comparable CRM platforms.
- Ability to discuss institutional outcomes with equal strength in data, business value, and educational mission.
##### HireNow Disqualifiers
##### The following will prevent candidates from moving forward:
- Jumpy resumes will not be accepted or interviewed.
- No direct college or university admissions/enrollment experience.
- Higher education experience limited to teaching, research, or other adjacent functions without admissions/enrollment responsibility.
- No demonstrated Account Executive or comparable closing experience.
- Inability to provide measurable evidence of sales performance or closed business.
- Limited experience engaging senior decision\-makers or managing multi\-stakeholder sales.
- Candidates unable to meet the New York City hybrid work requirement.
- Candidates unable to travel approximately 25%.
- Candidates who do not meet the core qualifications.
##### HireNow Staffing Disclaimer
##### HireNow Staffing is acting as a direct placement partner for this Higher Education AI Enrollment Growth \& Institutional Partnerships Executive opportunity. All candidate information is handled confidentially and evaluated against defined requirements. This job description outlines the general scope of responsibilities and qualifications. Duties may evolve based on client needs and business growth. Only candidates meeting the core qualifications will be considered for interview. Client\-specific information will be shared only with qualified candidates during the interview process.
##### https://www.careers\-page.com/hirenow\-staffing\-inc/job/X98976YY
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 HireNow Staffing, 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.
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.
HireNow Staffing AI Hiring
HireNow Staffing has 3 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $300K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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