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About This Role
At Bloomerang, we believe change happens on purpose. We champion the power and potential of nonprofits, igniting next\-level impact with the team and technology built for purpose. Our powerful giving platform and stellar support enable tens of thousands of nonprofits to raise more, recruit more, and retain more, fueling maximum impact and raising the bar on what's possible for the nonprofit sector. That's why, even as the nonprofit sector sees declines in giving, Bloomerang customers raise more year over year.
We're also in the business of creating thriving employees. Join a mission\-driven culture built on our core values of Simplify, Care and Act. We know our people are the key to our success, and we're proud to be home to some of the most innovative and skilled individuals in the workforce today. Come feel invigorated and unstoppable with us!
The Role
As the Sr. Paid Media Manager, you will own Bloomerang's paid acquisition strategy end\-to\-end. You are responsible for driving measurable pipeline and revenue growth through multi\-channel paid media programs including Google, LinkedIn, Meta, YouTube, CTV, Directories, and ABM platforms (including 6sense). You will define the strategic roadmap, manage agency performance, optimize channel mix, and partner cross\-functionally with Demand Gen, Customer, Brand, RevOps, and Sales to improve CAC, pipeline velocity, revenue, and ROI.
What You Will Do
#### Strategic Ownership
- Define and evolve Bloomerang's paid media strategy aligned to revenue targets and GTM goals.
- Develop annual and quarterly acquisition plans tied to pipeline and CAC goals.
- Lead testing roadmap across channels (creative, audience, bidding, messaging).
- Own the contribution percent of paid to the all\-in revenue targets.
#### Revenue \& Performance Accountability
- Own paid contribution to MQAs, SALs, pipeline, and revenue.
- Forecast spend, performance, and ROI.
- Identify scaling opportunities and efficiency gains.
- Optimize full\-funnel tracking in partnership with RevOps.
#### Agency \& Vendor Leadership
- Own agency relationship, scope, performance benchmarks, and accountability.
- Establish performance KPIs and reporting cadence.
- Evaluate and onboard new paid platforms or vendors as needed.
#### Cross\-Functional Leadership
- Partner with Sales, Customer, and Revenue teams to align on pipeline quality and revenue outcomes.
- Partner with external agency and internal Brand team on brand awareness paid media campaigns.
- Collaborate with Content and Brand teams on messaging strategy.
- Partner with Brand Services team to level up paid media creative to improve ad performance.
- Provide executive\-ready reporting on performance trends and insights.
#### Data \& Optimization
- Analyze performance weekly/monthly/annually across channels.
- Continuously refine channel mix based on ROI and pipeline contribution.
- Build dashboards and attribution insights to inform executive decisions.
#### Budget Management
- Own and optimize a significant paid media budget.
- Allocate spend dynamically based on performance signals.
- Drive efficient customer acquisition.
What You Need to Succeed
- Proven Growth Track Record: You bring 5–8\+ years of experience in performance or growth marketing, with a documented history of hitting CAC and ROI targets to drive measurable revenue.
- Strategic Budget Management: You are comfortable managing six\- or seven\-figure media budgets across a diverse mix of channels, including LinkedIn, Meta, YouTube, CTV, and ABM platforms (experience with 6sense is a major plus).
- Full\-Funnel Expertise: You have a deep understanding of demand generation, multi\-touch attribution, and the nuances of managing complex B2B sales cycles and audience segmentations.
- Data Modeling \& Analytics: You are highly proficient in forecasting performance, modeling CAC/LTV, and using tools like Salesforce, Marketo, and Google Analytics to optimize spend based on ROI.
- Executive\-Level Communication: You can translate complex performance data into clear, actionable insights and "executive\-ready" reporting for leadership.
- Agency \& Vendor Leadership: You know how to effectively manage and hold agency partners accountable, ensuring their output stays strategically aligned with business goals.
- Cross\-Functional Synergy: You excel at partnering with Sales, RevOps, and Content teams to sharpen pipeline quality and improve conversion rates across the entire funnel.
- Continuous Optimization: You possess a "test and learn" mindset, constantly running structured experiments to increase speed and consistency in knowledge creation and campaign performance.
- AI\-Forward Approach: You are eager to explore and responsibly integrate AI tools into your daily workflow to drive efficiency and support Bloomerang's broader mission.
Benefits
Health \+ Wellness
You'll have access to generous health, vision, and dental insurance options as well as HealthiestYou, a healthcare service that offers convenient, confidential access to quality doctors 24/7, anytime, anywhere.
Time Off
You'll get a competitive PTO package that includes 20 PTO days, 3 flex days, 4 optional volunteer days, 12 paid holidays, as well as paid parental leave. More is more!
401k
You'll receive a 401k match to help invest in your future.
Equipment
Everything you need to be successful, shipped right to your door. You got this. We got you.
Compensation
The salary range for this position is $103,000 \- $130,000\. You may also be eligible for a discretionary bonus. Actual compensation within the range will be dependent on your skills, experience, qualifications, and location, as well as applicable employment laws
Location
This is a permanent, full\-time, fully remote position (within the U.S. and select Canadian Provinces only). Employees living in Indianapolis, IN are welcome to work from our company headquarters. We do not offer Visa sponsorship or relocation assistance at this time.
Accommodations
Applicants who require accommodations may contact careers@bloomerang.com to request an accommodation in completing an application.
*Bloomerang is an Equal Opportunity Employer. Individuals seeking employment at Bloomerang are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, or sexual orientation.*
Salary Context
This $103K-$130K range is above 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 Bloomerang, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($116K) sits 30% below the category median. Disclosed range: $103K to $130K.
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.
Bloomerang AI Hiring
Bloomerang has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Remote, US. Compensation range: $130K - $160K.
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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