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About This Role
Email Marketing Specialist (Salesforce Account Engagement / Pardot)
Black Health Matters (BHM)
Remote Contract
About Black Health Matters
Black Health Matters is dedicated to advancing health equity by connecting underserved communities with trusted health information, resources, and opportunities. Through digital campaigns, events, and partnerships, we engage our audience in meaningful ways that support better health outcomes.
Role Overview
We’re looking for an Email Marketing Specialist with deep experience in Salesforce Account Engagement (Pardot) to own and optimize our email marketing ecosystem.
This role blends execution, automation, and strategy, you will manage campaigns end\-to\-end, build automated journeys, and ensure our email program drives engagement, education, and conversion.
You should be equally comfortable building emails, managing lists, troubleshooting issues, and analyzing performance.
What You’ll Do
Email Campaign Execution
Build, test, and deploy email campaigns using Salesforce Account Engagement (Pardot)
Manage:
- Newsletters
- Event promotions
- Sponsored campaigns
- Clinical trial awareness campaigns
- Pardot forms
Ensure all emails meet compliance requirements (CAN\-SPAM, unsubscribe handling, etc.)
Marketing Automation \& Journeys
Develop and manage:
- Engagement Studio programs (drip campaigns, nurture flows)
- Automated journeys based on user behavior and interests
Segment audiences using:
- Lists, tags, and behavioral data
- First\-party engagement signals (content interactions, form fills, etc.)
Implement lead scoring and grading models
List \& Data Management
Manage audience segmentation and suppression lists
Maintain data hygiene and ensure proper syncing between Salesforce and Pardot
Troubleshoot issues related to:
- List visibility
- Sync errors
- Email deliverability
- Unsubscribe and preference center configurations
Performance \& Optimization
Monitor and report on:
- Open rates
- Click\-through rates
- Conversions
- Engagement by segment
Analyze campaign performance and provide recommendations
Conduct A/B testing on subject lines, content, and send times
Optimize for both engagement and conversion
Cross\-Functional Collaboration
Partner with:
- Marketing team (campaign strategy)
- Design team (email creative)
- Content/editorial team (messaging)
- Client services team (sponsored campaigns)
Translate campaign goals into email strategy and execution
What We’re Looking For
Technical Expertise
2–5\+ years of experience in email marketing
- Hands\-on experience with:
- Salesforce Account Engagement (Pardot) REQUIRED
- Salesforce CRM (preferred)
Experience building:
- Dynamic lists
- Engagement Studio programs
- Custom redirects, forms, and landing pages
Platform Knowledge
Strong understanding of:
- Email authentication (SPF, DKIM, DMARC)
- Deliverability best practices
- Unsubscribe/preference center requirements
Experience troubleshooting Pardot\-specific issues:
- Merge fields and dynamic content
- List and suppression logic
- Email rendering inconsistencies
Marketing \& Strategy Skills
Understanding of full\-funnel email marketing
Experience with:
- Lead nurturing
- Conversion\-focused campaigns
- Behavioral segmentation
Ability to align email efforts with broader campaign goals
Analytical Mindset
- Comfortable working with campaign data and reporting
- Ability to translate performance metrics into actionable insights
Nice to Have
- Experience in healthcare, wellness, or mission\-driven organizations
- Familiarity with compliance considerations in healthcare marketing
- HTML/CSS knowledge for email customization
How You Work
- You are both technical and strategic
- You don’t just “send emails” — you build systems that scale
- You’re proactive in identifying issues and optimizing performance
- You understand that email is a key driver of engagement and trust
Impact of This Role
Your work will directly influence how our audience:
- Receives important health information
- Engages with community resources
- Learns about events and opportunities
You will help ensure the right message reaches the right person at the right time.
Compensation
- Competitive hourly rate $35\-$45 USD per hr. (based on experience)
Pay: $35\.00 \- $45\.00 per hour
Work Location: Remote
Salary Context
This $72K-$93K 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Black Health Matters, 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 ($83K) sits 50% below the category median. Disclosed range: $72K to $93K.
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.
Black Health Matters AI Hiring
Black Health Matters has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $93K - $93K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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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