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About This Role
EverCommerce (Nasdaq: EVCM) is a leading service commerce platform, providing vertically-tailored, integrated SaaS solutions that help more than 690,000 global service-based businesses accelerate growth, streamline operations, and increase retention. Its modern digital and mobile applications create predictable, informed, and convenient experiences between customers and their service professionals. With its EverPro, EverHealth, and EverWell brands specializing in Home, Health, and Wellness service industries, EverCommerce provides end-to-end business management software, embedded payment acceptance, marketing technology, and customer experience applications. Learn more at EverCommerce.com.
We are building an extraordinary company and looking for talented, energetic, and motivated people to join our team. You can learn more about our Company, Culture and Values here: https://www.evercommerce.com/about-us/careers/
We are currently seeking rising junior and senior students to apply for our summer internship program. This is a great opportunity for individuals looking to gain hands-on experience and contribute to meaningful projects.
We are looking for an AI Automation Intern to join our Business Operations team. Reporting to the VP of Operations, you’ll partner closely with Revenue Operations, Marketing, and Sales to build AI-centric automations that improve speed, consistency, and scalability across go-to-market workflows.
This role is ideal for someone who loves turning messy processes into streamlined systems—and can combine strong AI literacy with practical automation skills. You’ll focus on shipping 1–3 meaningful automations during the internship, with the opportunity for your work to progress from prototype to production.
Responsibilities:
- Build AI-centric automations using automation platforms (e.g., Workato and similar tools) to improve GTM execution (routing, handoffs, enrichment, task creation, follow-ups, workflow orchestration, etc.).
- Prototype and iterate quickly: define the workflow, build the automation, test with users, refine, and prepare for handoff.
- Collaborate with stakeholders to define success metrics (time saved, error reduction, adoption) and report outcomes.
- Create clear documentation (process maps, SOPs, runbooks, and admin guides) to enable long-term ownership and maintenance.
- Participate in the company-wide intern program activities and team meetings, providing updates and final outcomes.
Qualifications:
- Pursuing a bachelors degree in Engineering or Business Administration.
- High level of attention to detail/accuracy.
- Exercise discretion and good judgment and maintain confidentiality when handling sensitive information.
- Positive attitude approaching any task no matter how big/small.
- Approaches projects with creativity and is willing to think outside of the box.
- Effective communication skills, both written and verbal, with the ability to present complex information in a clear and concise manner.
- Proactive, self-motivated, and able to work both independently and collaboratively in a fast-paced environment.
- Willing to travel for Denver, Colorado for the intern kickoff week and possibly the Intern executive presentations.
What we are looking for:
- Rising juniors and seniors pursuing a bachelor’s degree from an accredited university.
- Applicants to be currently authorized to work in the United States on a full-time basis from June to August 2026.
- Must be willing to commit to a 10-week internship the Summer of 2026 (June-August) on a full-time basis.
- Enthusiastic about technology and committed to professional development.
- Willing to learn and collaborate with your teammates.
Where:
The EverCommerce team is distributed globally, with teams in the U.S., Canada, the U.K., Jordan, New Zealand, and Australia. With a widely distributed team, we are used to working remotely across different time zones. This role can be based anywhere in the United States – if you’re close to one of our offices, we can set you up in-office or you can work 100% remotely. Please note that you must be eligible to work without sponsorship to qualify for this position.
Compensation:
The target base compensation for this position is $20.00 USD per hour. Final offer amounts are determined by multiple factors including location, local market variances, and candidate experience and expertise, and may vary from the amounts listed above.
EverCommerce is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender identity, sexual orientation, age, marital status, veteran status, or disability status. We look forward to reviewing your credentials and getting to know more about your experience!
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 3,897 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Evercommerce, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Evercommerce AI Hiring
Evercommerce has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $160,000 across 1,226 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 3,897 open positions tracked in our dataset. By seniority: 111 entry-level, 1,958 mid-level, 1,413 senior, and 415 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (615 positions). The remaining 3,251 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 3,897 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,733), Data Scientist (273), AI Software Engineer (271). 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 (111) are outnumbered by mid-level (1,958) and senior (1,413) 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 415 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (615 positions), with 3,251 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (2,064 postings), Aws (1,085 postings), Azure (867 postings), Rag (865 postings), Gcp (697 postings), Pytorch (650 postings), Prompt Engineering (597 postings), Kubernetes (499 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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