Interested in this AI/ML Engineer role at Bloomerang?
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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 an AI Platform Engineer within our System Administration team, you will design and build the internal AI platform that every Bloomerang employee uses to do their best work. This role isn't just about maintaining infrastructure — it's about shipping a product. You will architect a unified AI workspace where authentication, integrations, governed data access, and shareable skills come together so every employee can be an AI power user without configuring a thing.
Beyond the platform build, you'll also have an active hand in advancing Bloomerang's adoption of existing internal AI tools — Replit, Claude, Gemini, Skyvern, and others — partnering with our AIOps Engineer to keep the broader ecosystem moving forward.
Why This Role Matters: The AI models are already exceptional. The problem isn't intelligence — it's the harness. Most employees are driving a Ferrari with the handbrake on, not because they lack ambition, but because they've never seen what a well\-configured AI environment can do. You're the person who builds that environment. Internal AI productivity is becoming a competitive moat, and this role owns the moat.
What You Will Do
- AI Platform Build
+ Ship the Product: Design and build Bloomerang's internal AI platform end\-to\-end, from architecture through iteration.
+ Frictionless Access: Build a unified workspace where employees sign in once and every tool, integration, and capability is ready on day one.
+ Governed Intelligence: Partner with data and security stakeholders to integrate role\-based, just\-in\-time access to a tiered data model.
+ Marketplace of Skills: Build the internal app store where employees can publish, discover, and one\-click install reusable skills, flows, and agents.
+ Memory That Works: Architect a context system that lets the platform know what people are working on without asking them to re\-explain.
- Internal AI Tooling Enablement
+ Hands\-On With the Stack: Partner with the AIOps Engineer to advance Bloomerang's adoption and capability with Replit, Claude, Gemini, Skyvern, and other AI tools already in use.
+ Builder Support: Help internal builders get the most out of existing tools through templates, examples, and direct technical partnership.
- Embedded Discovery
+ Sit With Users: Spend time with employees across departments to see how they actually work and where the platform can move the needle.
+ Translate Into Capability: Turn what you observe into shipped features — primitives others can build on, not one\-off solutions.
What You Need to Succeed
- The Builder Mindset: You ship. You'd rather have something rough running in production than something perfect on a whiteboard.
- Mixed AI \& Generalist Depth: You've built with LLMs and agentic frameworks, and you're a strong generalist software engineer.
- Platform Thinking: You build for the second user, not just the first. One person's breakthrough should become everyone's baseline.
- Modern AI Tooling Fluency: Working knowledge of Claude Code, Replit, MCP, and the broader agentic ecosystem.
- Identity \& Security Fluency: Comfortable integrating SSO (OIDC/SAML) into custom applications and reasoning about least\-privilege access.
- Communication: You translate user needs into technical solutions without losing the nuance — and explain what you built back in their language.
- Adaptability: The AI landscape shifts weekly. You're energized by that, not exhausted by it.
- High Ownership: Greenfield build, small team. You make decisions, ship, and adjust.
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 $127,000 \- $153,000\. 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 [email protected] 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 $127K-$153K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($140K) sits 22% below the category median. Disclosed range: $127K to $153K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Bloomerang AI Hiring
Bloomerang has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $153K - $153K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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