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About This Role
Job description:
The Enrollment Specialist / Office Manager is in charge of recruiting, selecting, and enrolling eligible families within their center\-based location. He/She also creates, provides, and coordinates services and activities with families and communities that foster strength, healthy living, and overall well\-being. The Enrollment Specialist / Office Manager is responsible for promoting the vision for quality early childhood services that anchor the organization.
Essential Skills:
1\. Experienced with data entry, both through various software programs and familiarity with organizing paper files.
2\. Keen attention to detail, with the patience required to follow tedious data entry procedures.
3\. Tech\-savvy, with proficiency in Microsoft Office (including Excel) and ability to navigate various enrollment software.
4\. Team spirit, with a willingness to fulfill administrative directives that best serve a broader vision for program success.
5\. Strong interpersonal skills, including the ability to build professional and collaborative relationships with colleagues and members of the community at large.
6\. Patient and proactive, as best demonstrated through flexibility in day\-to\-day operations and the capacity to deliver formative and thoughtful feedback.
7\. Robust communication skills—both verbal and written.
8\. Self\-motivated, with a strong sense of personal accountability.
MUST HAVE EXPERIENCE WITH: AND
Essential Responsibilities (Includes but, is not limited):
1\. Work collaboratively with the Building Principal, Recruitment Manager, and Director of Enrollment to ensure that the greatest number of eligible children participate in the program as currently funded slots allow.
a. Identify and recruit eligible families and children, including children with disabilities and underserved populations.
b. Assist with the organization of marketing and/or recruitment activities, such as tabling events and/or center\-based open houses—coordinating all necessary materials and scheduling, under the purview of the Building Principal.
c. Network and build a collaborative partnership with local community\-based organizations, with the aim of building a base for student referrals for enrollment.
d. Canvas neighborhood to market center\-based location for enrollment and engage within the community to help establish the center\-based location within the nearby vicinity. Some examples include:
i. Create and hand out flyers
ii. Host community events
iii. Lead family tours
iv. Follow\-up with interested families via phone and email
e. Prepare to spend extended periods of time outside of the facility in order to actively engage with prospective families and effectively network with local community hubs that are both trusted and frequented within the community.
f. Review weekly plans for community outreach with the Building Principal, regularly sharing all schedules for planned time outside of the center\-based facility.
g. Stay informed on all enrollment regulations and requirements as they pertain to all relevant funding streams, including but not limited to HRA Vouchers, Office of Head Start (OHS) enrollment, DOE and UPK Classroom Enrollment and private tuition\-based payments.
h. Successfully meet weekly enrollment goals, as established through collaboration with the Building Principal.
2\. Maintain proper documentation of all pertinent child and family information in order to best prepare for enrollment and effective documentation of all subsequent service provisions.
a. Demonstrate a strong understanding of ChildPlus, ProCare, and CRM.
b. Maintain a comprehensive understanding of all necessary child documentation, as required by all applicable regulatory agencies and detailed within each service area’s child file checklist.
c. Enter and track all leads for enrollment into the CRM database.
d. Create initial child files within appropriate databases, including paper files.
e. Maintain daily communication with other center\-based leadership.
f. Provide follow\-up when a family has not fully committed to the completion of an enrollment application.
g. Provide appropriate follow\-up when additional documentation or confirmation is needed from families. (e.g., child absence, application materials, the date for the tour, on\-site interview)
3\. Work diligently to facilitate active parent involvement, fostering a trusting and collaborative partnership with each family.
a. Acclimate families to the program according to the established orientation plan.
b. Organize and actively participate in Parent Committee Meetings.
c. Plan monthly Family Involvement Activities.
d. Help families identify and access needed resources. (e.g., health, social services, job training, etc.)
e. Provide support to families of children with disabilities.
4\. Maximize community resources through collaborations.
a. Facilitate the delivery of services to children and families through collaboration with community partners.
b. Actively participate in community resource planning and related work to establish strong partnerships.
c. Network and maintain relationships amongst relevant service provides within the community.
5\. Exhibit flexibility and teamwork in day\-to\-day operations and in providing needed services to families.
a. Actively participate in two\-way communication among co\-workers to ensure all staff members are informed of pertinent information, as it pertains to the center operations, enrollment updates, and classroom activities.
b. Provide resources to new and existing families. (e.g., recommendations for food banks or shelters)
c. Clocking children in and out of ProCare as needed.
d. Answering phones, scanning, filing, and emailing documents as needed.
e. Actively participate in and contribute to classroom coverage on an as needed basis.
f. Participate in activities designed to ensure program quality.
Essential Outcomes:
1\. Continuous full enrollment
2\. Maintaining an active waitlist for enrollment
3\. Sustaining all funding streams, including Vouchers, Tuition Payments, and OHS enrollment
4\. Establishing collaborative community relationships that increase overall enrollment within the center
The Enrollment Specialist / Office Manager is expected to perform any new or modified functions as delegated by his/her immediate supervisor, the Building Principal, or any other member of the leadership team.
Job Type: Full\-time
Salary Context
This $45K-$47K range is in the lower quartile 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 Beanstalk Academy, 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. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($46K) sits 72% below the category median. Disclosed range: $45K to $47K.
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.
Beanstalk Academy AI Hiring
Beanstalk Academy has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Brooklyn, NY, US. Compensation range: $47K - $47K.
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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