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About This Role
Location: Los Angeles, California (on-site)
Our mission is to promote American values through the creative use of digital media, technology and edu-tainment. We're proud to be voted among the “Best Place to Work in Los Angeles” by the LA Business Journal 4 years in a row.
This role leads our online membership growth and retention initiatives while managing key donor communications projects across the organization. The Director, Membership & Donor Fulfillment will collaborate closely with leadership and cross-functional teams to execute merchandising campaigns, oversee fulfillment-related deliverables and logistics, and ensure donor data accuracy and an excellent supporter experience. The ideal candidate is highly organized, collaborative, and able to manage multiple priorities at once.
Duties and Responsibilities:
- Support PragerUnited membership initiatives through ongoing collaboration with the CMO and CEO for key programs such as PragerUnited
- Maintain and grow membership with a strong focus on retention and supporter engagement
- Drive membership acquisition through outreach campaigns such as email, direct mail, and calling efforts
- Manage the quarterly gift selection process for PragerUnited, including coordination of product creation and fulfillment timelines and managing all logistics for delivery
- Manage ongoing donor giveaway campaigns and execute all operational logistics needed
- Create ongoing reports to assess program performance and identify opportunities for improvement
- Oversee customer service support efforts for membership-related inquiries
- Project manage mass donor mailings as needed
Successful Candidates Will Possess:
- 5-7+ years of experience in project management, marketing operations, donor communications, membership programs, or a related role
- Strong organizational skills with the ability to manage multiple projects, deadlines, and stakeholders simultaneously
- Excellent written and verbal communication skills, with experience creating or managing supporter-facing messaging
- Comfortable working cross-functionally with executive leadership, design, development, and database teams
- Detail-oriented with a strong focus on accuracy, especially with donor information and reporting
- Experience coordinating printed mail, email communications, and outreach campaigns
- Happy to spend much time and effort in merchandising and logistics for donor incentives (product selection, procurement, shipping logistics, etc.) for large database of supporters
- Strong analytical and reporting skills, including the ability to track performance and recommend improvements
- Proficiency with standard office tools (Google Workspace or Microsoft Office); CRM/database experience is a plus
- Collaborative, service-minded, and able to adapt quickly in a fast-paced environment
How to Apply: First review our website at www.prageru.com (http://www.prageru.com/). Read our annual report here (https://downloads.ctfassets.net/qnesrjodfi80/1E7ztqeYZ7BK92LzbkGGXJ/50f1cba968c3b5dd9ebe125b2a025141/PragerUAnnualReport\_Digital\_2025.pdf). Submit a resume and cover letter. In your cover letter, include a section explaining how your values align with ours, and why you would want to specifically work at PragerU.
What We Do: We promote American values through educational videos for people of all ages. People come to PragerU for a variety of reasons, but they all have one thing in common: They want to grow—intellectually, spiritually, emotionally, and physically. To learn more about PragerU, visit http://prageru.com.
Salary Range: The salary target for this role is $100,000 - $125,000+. Final offer amounts depend on multiple factors including candidate experience and expertise, and most recent market data. This position is eligible for an annual bonus based on personal and company performance, in addition to our robust benefits package.
We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, gender identity, disability, protected veteran status, or any other characteristic protected by law.
An error page could appear for several reasons. If a technical issue occurs while applying, we suggest double checking a few things. Click here for additional information (https://drive.google.com/file/d/1fuTleiCec3LQ\_DiEvKmrnFIsia69g2Fx/view?usp=sharing).
*Please note that we constantly have ideas and concepts pitched to us which we appreciate. Most ideas and concepts are not protectable and are freely available for the public and PragerU to use or modify and use. From time to time where they are not, you agree that by presenting these ideas and concept, and in consideration of PragerU reviewing these ideas and concepts, you hereby grant to PragerU an irrevocable, worldwide, royalty-free and non-exclusive license to use, modify and exploit for any purpose any ideas and concepts and any expressions of those ideas and concepts. Nothing herein precludes you from using any ideas or concepts presented.*
Salary Context
This $100K-$125K range is below the median 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At PragerU, 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 ($112K) sits 27% below the category median. Disclosed range: $100K to $125K.
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.
PragerU AI Hiring
PragerU has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $125K - $125K.
Location Context
AI roles in Los Angeles pay a median of $179,440 across 1,356 tracked positions. That's 6% below the national 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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