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About This Role
Are you looking for a high energy, strategic, and fast\-paced position as a Email Marketing Manager? Join Relias, the company changing lives throughout the world by helping healthcare organizations improve their clinical and financial outcomes!
For 11,000\+ health care and human service organizations, Relias helps clients deliver better clinical and financial outcomes by elevating the performance of teams. We help organizations across the continuum of care get better at maintaining compliance, developing staff and promoting consistent, high\-quality care. Our platform employs assessments to reveal specific gaps in skills and addresses them with personalized and engaging learning, choosing from 7,000\+ online courses that meet accrediting board, state and federal requirements. We are passionate about our products and our clients; what we deliver and the impact we have on the world is truly something you can be proud to represent. Join us and make a difference.
WHAT CAN RELIAS OFFER YOU?
- Fantastic health and wellness benefits package, including an outstanding 401k match, a flexible PTO program, and a generous and inclusive parental leave policy. Additionally, Relias pays for the employee portion of the monthly healthcare premium!
- Flexible work environment with onsite and work from home options – you choose when you want to come into the office!
- Active Employee Resource Groups open to all employees!
- Comprehensive onboarding program – a great introduction to our company, customers and culture!
- Growth and career advancement opportunities!
+ Multiple development program options – leadership development, professional development curriculums, and Nanodegree options in both technology and data science
+ Professional development gained from conference attendance and participation in organizations like NC Tech
Onsite 321 Coffee Shop providing free coffee and pastries to employees
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The B2B Email Marketing Manager is responsible for planning, developing, implementing, and optimizing data\-driven email marketing programs that support lead generation, pipeline acceleration, and customer lifecycle marketing within a B2B environment. This role partners closely with Demand Generation, Sales, Lead Operations, Digital, and other stakeholders to execute targeted campaigns and multi\-step nurture programs using Pardot (Marketing Cloud Account Engagement) and Salesforce (SFDC). The Email Marketing Manager ensures email initiatives align with revenue goals, customer success, partner marketing, and sales processes, list building/segmentation strategy, and marketing automation best practices while maintaining strong governance, reporting accuracy, and brand consistency.
WHAT YOU’LL DO:
- Independently design, build, QA, and deploy B2B email campaigns in Pardot (Marketing Cloud Account Engagement)
- Develop, execute, and monitor multi\-step, behavior\-based nurture programs aligned to verticals, lifecycle stages, and go\-to\-market initiatives
- Support promotion of Demand Generation initiatives including webinars, gated content, events, product launches, and pipeline acceleration campaigns
- Build and maintain dynamic lists, segmentation rules, and automation rules to support verticalized and multi\-product marketing strategy
- Apply a basic understanding of email nurture execution to support scalable lifecycle programs
- Develop, implement, and track A/B testing strategies for subject lines, content, CTAs, send timing, and audience targeting
- Interpret marketing performance data (open rates, CTR, CTOR, conversion rates, MQLs, influenced pipeline) and adjust campaign strategy accordingly
- Develop performance reports and dashboards to communicate results to stakeholders and leadership at a regular cadence
- Maintain intermediate knowledge of email best practices including deliverability, CAN\-SPAM compliance, data hygiene, and mobile optimization
- Use HTML/CSS to update email templates and HTML blocks while maintaining brand and responsive standards
- Test and troubleshoot email rendering, tracking, and automation issues across devices and clients
- Maintain strong data governance standards within Pardot and SFDC, including list validation and suppression management
- Support broader marketing operations initiatives including Pardot form creation, custom tracking link (UTMs) \+ redirect generation, and other automation assets to enable campaign execution and accurate attribution
- Work effectively in a matrixed organization, partnering with Demand Gen, Lead Ops, Sales, Creative, and Digital teams
- Follow established approval processes and manage stakeholder timelines to ensure on\-time delivery
- Demonstrate excellent time management skills and the ability to prioritize multiple concurrent campaigns
- Contribute to broader B2B lifecycle marketing strategy and campaign planning
- Mentor junior team members in email build best practices, QA processes, automation strategy, and performance analysis; be viewed as a team leader and subject matter expert
YOU’VE GOT WHAT IT TAKES IF YOU HAVE:
- Bachelor’s Degree in Marketing, Communications, Business, or related field
- 4\+ years of digital marketing experience
- 3\+ years developing and executing B2B email marketing campaigns
- 2\+ years hands\-on experience with Pardot (Marketing Cloud Account Engagement) or similar marketing automation platform
- Experience working within Salesforce (SFDC) environment
- Intermediate understanding of marketing automation platforms (Pardot/Marketing Cloud Account Engagement, Marketo, HubSpot, SFMC, Iterable, etc.) including segmentation, automation rules, engagement programs, and reporting
- Strong working knowledge of Salesforce (SFDC) data structure, including lead vs. contact
- architecture, campaign tracking, and simple reporting
- Ability to maintain accurate data, workflows, and documentation within a B2B marketing operations framework
- Ability to build and optimize lifecycle\-based email programs that support lead generation, MQL conversion, sales enablement, and customer retention
- Applies intermediate knowledge of email best practices to improve engagement, deliverability, and pipeline impact
- Strong written communication skills with the ability to craft clear, compelling, and audience\-specific B2B messaging aligned to verticals and buyer personas
- Ability to analyze performance metrics and translate data into actionable optimizations that support revenue growth
- Understands how email performance contributes to broader marketing and sales KPIs
- Ability to work effectively in a matrixed organization with cross\-functional stakeholders
- Demonstrates leadership by mentoring team members in building emails, executing strategy, and improving processes
- Strong organizational and time management skills with the ability to manage competing priorities
IT WOULD BE NICE IF YOU:
- Advanced HTML/CSS for email development
- Experience working in a verticalized B2B marketing structure
*Relias is an Equal Opportunity Employer and a Drug\-Free workplace*
IN OFFICE REQUIREMENT:
Relias values collaboration and wants to ensure that our team members have opportunities to work with their teams regularly for professional development opportunities. Our flexible hybrid work environment requires that you live in the state of North Carolina, within a commutable distance to our office (\~1\-hour commute). You would be expected to work in our Morrisville, NC Headquarters approximately 30/40 days/quarter.
Company: Relias LLC \| Job ID: 287696
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 Relias, 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.
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.
Relias AI Hiring
Relias has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Morrisville, NC, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 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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