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Job Title: AI\-Enabled Editor
Employment Type: Full\-Time, 40 hours/week
Reports to: Communications Manager
FLSA Status: Exempt
Position Type: In\-person
Who We Are
SurgeU is a mission\-driven parent company overseeing a family of brands, including Life Surge, focused on faith\-based business education and empowerment. One of the fastest\-growing organizations in the country, Life Surge/SurgeU exists to inspire, train, and equip people to build their personal impact in ways that glorify God. By producing 30\+ annual events and providing financial education to thousands around the nation, we do just that.
We are a team of experienced professionals who are passionate about helping people learn, grow, and connect so they may live more enriched lives. Our culture is one where we celebrate each other, individually and as a team. We look to acknowledge and reward our star performers. Let your light shine in our company!
Opportunity:
SurgeU is looking for an AI\-Enabled Editor who can help us produce high\-quality content at the speed a modern communications organization requires. This is not a traditional copywriting role, and it is also not a role where AI replaces writing.
We are looking for someone who understands how to use AI as a professional editorial tool\- to accelerate drafting, research, iteration, repurposing, and content production\- while maintaining human ownership of accuracy, judgment, voice, clarity, and credibility.
Responsibilities:
- Drafting and editing press releases, media statements, executive talking points, FAQs, web content, bios, student stories, social copy, and other communications materials.
- Using AI tools to accelerate first drafts, rewrites, summaries, repurposing, ideation, research organization, and content adaptation.
- Taking ownership of the quality of every piece of content you touch, regardless of whether the first draft was written by you, another team member, or an AI system.
- Editing content for clarity, structure, tone, accuracy, brand voice, and strategic intent.
- Working directly with the Manager, Earned Media \& Communications to translate messaging priorities into finished content.
- Supporting the Narrative \& Content Strategist and other cross\-functional partners when messaging architecture needs to be translated into copy.
- Adapting core messages across multiple formats and stages of the customer journey.
- Turning complex source material into clear, compelling, human language.
- Developing multiple versions of content based on audience, channel,objective, and level of awareness.
- Fact\-checking quotes, statistics, outcomes, names, titles, claims, and other factual content before publication.
- Maintaining a clear distinction between what is verified, what is approved, and what requires additional substantiation.
- Managing and expanding SurgeU’s library of approved claims, proof points, quotes, statistics, FAQs, and reusable language.
- Helping maintain the SurgeU brand voice and editorial style standards.
- Developing guidance for how AI should and should not be used in communications workflows.
- Building, testing, and improving prompts that produce more useful first drafts and reduce unnecessary editing time.
- Identifying recurring editorial problems and improving the workflow rather than repeatedly fixing the same issue manually.
- Partnering with ORM and SERP teams on trust content, FAQs, reputation pages, and other search\-sensitive copy.
- Supporting rapid\-response communications when the organization needsaccurate, high\-quality copy quickly.
- Repurposing strong source material into multiple formats without losing accuracy or context.
Qualifications:
- 3\+ years of professional experience in copywriting, editing, communications, brand content, journalism, or a related field.
- Bachelor's degree in Communications, Public Relations, Journalism, Business, or a related field is a plus.
- Excellent writing and editing skills across short\-form and long\-form content.
- Demonstrated ability to write in multiple formats and voices.
- Hands\-on experience using AI writing tools as part of a professional workflow.
- Strong prompting skills and an understanding of how to improve AI outputs through context, structure, examples, and iteration.
- Excellent fact\-checking instincts.
- Strong editorial judgment around sensitive or reputation\-relevant claims.
- Ability to distinguish between a stylistic edit and a substantive factual issue.
- Strong understanding of brand voice and how tomaintainconsistency at scale.
- Ability to work from raw source material, interviews, transcripts, briefs, and internal documents.
- Strong organizational skills and attention to detail.
- Ability to work quickly whencommunicationsneeds become urgent.
- Comfort receiving feedback and making rapid revisions.
- Strong curiosity about AI, content systems, and emerging editorial workflows.
Job Benefits:
- Health, Dental, Vision, Life, Holiday, and Paid Time Off.
- Non\-corporate, casual, entrepreneurial, comfortable, fun, and proactive work environment.
- High\-level performers, disciplined, and self\-motivated people will do very well in this environment.
*Life Surge/SurgeU is an Equal Opportunity Employer. We value diversity and seek to empower each individual while supporting the many perspectives, skills, and experiences within our workforce. All employment is decided based on qualifications, merit, and business needs.*
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 SurgeU, 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 in Demand for This Role
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.
SurgeU AI Hiring
SurgeU has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palmetto, FL, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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