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About This Role
Company Overview
The Compliance Services Organization didn’t exist. We built it.
MP1 Solutions pioneered the Compliance Services Organization (CSO) — a new category of operating partner built to replace the fragmented vendor model that has defined compliance programs for decades. Built on the foundation of MedPro Disposal, the nation’s largest privately\-held medical waste company serving 35,000\+ providers across 48 states over the past 15 years, MP1 Solutions extends that operational infrastructure, vendor network, and customer trust into a category we’re actively building — and leading.
What we do: We operate as the single compliance partner for non\-acute healthcare facilities — consolidating every obligation into one coordinated program, assigning one dedicated account manager as the point of accountability, and actively managing the underlying vendor ecosystem through continuous evaluation, negotiation, and optimization on the practice’s behalf.
Why it matters: Regulated facilities across the country are managing compliance the same fragmented way they always have. We’re changing that — and we’re looking for people who want to help build what comes next.
Our technology: We’re actively building the AI infrastructure that sits behind this model — automating compliance tracking, surfacing risks before they become problems, and creating a system that gets smarter with every client we add.
Our impact: Our clients never have to worry about something falling through the cracks — obligations are coordinated, tracked, and documented in one place. We audit costs, eliminate overcharges, and deliver proof of compliance that always keeps practices inspection ready.
Position Overview:
The AI\-Driven Marketing Coordinator is a key executional force within MP1 Solutions' marketing team, responsible for planning, launching, and optimizing multi\-channel campaigns that drive brand awareness, lead generation, and pipeline growth. This role bridges strategy and execution — translating campaign plans into action using a modern martech stack including AI\-powered tools, Salesforce/Pardot, and digital content platforms. Success in this role means campaigns launch on time, performance data drives decisions, and marketing's contribution to sales pipeline is measurable and growing.
Key Responsibilities:
- Campaign Execution \& Management — Plan, build, and launch digital marketing campaigns across email, landing pages, paid channels, and social. Manage campaign calendars and ensure on\-time, error\-free delivery.
- Marketing Automation \& CRM — Build and maintain email workflows, audience segments, and lead nurturing sequences in Pardot (Salesforce Marketing Cloud). Coordinate list hygiene, lead scoring, and tagging with the sales team.
- AI Tool Integration — Research, test, and implement AI\-powered marketing tools to enhance content creation, audience targeting, lead enrichment, and campaign performance. Document outcomes and share learnings with the team.
- Landing Page \& Web Coordination — Manage and update landing pages in Unbounce and WordPress. Collaborate with designers and developers to deploy forms, tracking pixels, and A/B test variants.
- Cross\-Functional Collaboration — Partner with the sales team, BDRs, and leadership to align campaign messaging with pipeline needs. Act as the marketing point of contact for campaign timelines, assets, and status updates.
- Analytics \& Reporting — Track and report on campaign KPIs including open rates, click\-through rates, lead conversions, and pipeline contribution. Deliver regular performance summaries to marketing leadership.
- Project \& Asset Coordination — Manage campaign tasks, timelines, and asset requests in Asana. Coordinate with content writers, graphic designers, and external vendors to ensure quality and consistency.
Qualifications:
Required
- 3–5 years of experience in marketing, campaign management, or marketing operations
- Hands\-on experience with Salesforce Marketing Cloud Account Engagement (Pardot) or comparable marketing automation platform
- Proficiency in WordPress and/or Unbounce for landing page management
- Strong project management skills with experience using tools like Asana, Monday.com, or equivalent
- Analytical mindset with the ability to interpret campaign performance data and recommend improvements
- Excellent written and verbal communication skills; ability to collaborate with sales, design, and leadership
- Must be US\-based and authorized to work in the United States
Nice\-to\-Have:
- Experience with AI\-powered marketing tools (e.g., Clay, Jasper, ChatGPT for marketing workflows)
- Familiarity with Salesforce CRM beyond Pardot — reporting, lead tracking, opportunity management
- Experience with paid digital advertising (Google Ads, LinkedIn Ads, Meta Ads)
- Background in B2B marketing, healthcare, or compliance\-adjacent industries
- Exposure to tools like Go High Level, Webflow, Zapier, or Outreach.io
- Bachelor's degree in Marketing, Communications, Business, or related field (or equivalent experience)
Compensation \& Benefits:
- Base salary range $60,000\-$75,000 based on experience, plus additional earnings opportunities to the successful implementation of marketing campaigns and AI\-focused solutions.
- Comprehensive benefits package including health, dental, and vision insurance, paid time off, and more.
- Be part of an innovative, growth\-focused company in the healthcare industry.
- Work in an environment where your ideas and contributions make an impact.
- Access to the latest tools and resources to expand your skills and experience.
- A supportive team that values your curiosity and drive for learning.
Application Process:
Shortlisted candidates will be scheduled for initial phone screening and will be required to complete a brief skills assessment before the first interview.
*MedPro Disposal and MP1 Solutions are equal opportunity employers. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, or any other characteristic protected by law.*
Salary Context
This $60K-$75K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At MedPro Disposal, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($67K) sits 63% below the category median. Disclosed range: $60K to $75K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
MedPro Disposal AI Hiring
MedPro Disposal has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Naperville, IL, US. Compensation range: $75K - $75K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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