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About This Role
Campaign \& Content Lead (Growth Marketing)
(Part\-Time, Hybrid\-NJ)
Own messaging, outbound campaigns, and content that directly drives pipeline for a cybersecurity company.
About Cedonix
Cedonix is a cybersecurity\-focused MSP/MSSP delivering white\-glove, compliance\-driven security solutions to finance and healthcare organizations.
We specialize in:
- SOC as a Service (SOCaaS)
- Endpoint Detection \& Response (EDR)
- Compliance\-driven cybersecurity (HIPAA, financial regulations)
Our mission:
Seamless, strategic, scalable security…simplified.
The Role
We are seeking a highly capable, part\-time Growth Creative / Marketing Operator to own messaging, campaigns, and creative execution across outbound and digital channels.
This is NOT a traditional social media or design role.
You will operate as a hybrid between a marketer, copywriter, and creative strategist, helping drive real pipeline and revenue.
Responsibilities
Messaging \& Campaign Development
- Develop compelling, ROI\-driven messaging for:
- Finance and healthcare audiences
- Translate technical cybersecurity services into clear business value
Outbound Campaign Support
- Create and refine outbound email sequences with Sales \& Marketing teams
- Improve response and conversion rates from lead generation efforts
- Develop new messaging angles weekly in conjunction with Sales \& Marketing
Content \& Creative Execution
- LinkedIn content (thought leadership, campaign\-driven posts); manage vendor creative services and campaign
- Coordinate creation of Sales one\-pagers and supporting collateral with Vendors
- Email copy and light landing page content
AI \& Automation Integration
- Engage with AI tools (ChatGPT, automation platforms) to:
- Generate campaigns
- Personalize outreach
- Streamline workflows
- Analyze ICP engagement
Requirements (NON\-NEGOTIABLE)
- Strong B2B copywriting experience
- Proven ability to create conversion\-focused messaging
- Experience working with technical or complex services (SaaS, cybersecurity, IT, etc.)
- Ability to think strategically (not just execute)
- Proficiency with:
- Canva and/or Figma
- AI tools (ChatGPT or similar)
Preferred
- Experience with HubSpot
- Familiarity with cybersecurity or compliance industries
- Prior experience working with multiple vendors and creative teams and SaaS platforms
- Experience supporting outbound / SDR teams
Compensation
- Part\-time (10\-15 hours/week)
- Long\-term engagement preferred
*What Success Looks Like*
- Increased outbound response rates
- Stronger messaging clarity
- Consistent, high\-quality campaign execution
- Tangible contribution to pipeline growth
Please submit portfolio, weblinks, etc. along with contact information, availability (Start Date, working schedule), and location.
Candidates local to Northern/Central NJ/ NYC region highly preferred.
Pay: $25\.00 \- $30\.00 per hour
Work Location: Hybrid remote in NY, NY 10016
Salary Context
This $52K-$62K range is below the median 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 10,872 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Cedonix LLC, 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. Senior-level AI roles across all categories have a median of $225,000. This role's midpoint ($57K) sits 63% below the category median. Disclosed range: $52K to $62K.
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.
Cedonix LLC AI Hiring
Cedonix LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $62K - $62K.
Location Context
AI roles in New York pay a median of $204,100 across 1,633 tracked positions. That's 7% above 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 10,872 open positions tracked in our dataset. By seniority: 871 entry-level, 6,773 mid-level, 2,013 senior, and 1,215 leadership roles (Director, VP, C-Level). Remote roles make up 11% of the market (1,212 positions). The remaining 9,626 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 10,872 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (9,872), AI Software Engineer (262), Data Scientist (256). 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 (871) are outnumbered by mid-level (6,773) and senior (2,013) 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 1,215 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 11% of all AI roles (1,212 positions), with 9,626 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: Python (1,782 postings), Aws (1,065 postings), Azure (796 postings), Rag (710 postings), Gcp (617 postings), Pytorch (602 postings), Prompt Engineering (516 postings), Tensorflow (511 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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