Interested in this AI/ML Engineer role at Evercommerce?
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About This Role
At EverCommerce \[Nasdaq: EVCM], we are on a mission to digitally transform the service economy with tailored, end\-to\-end SaaS solutions that simplify and empower the lives of our 725,000\+ customers. As a leading service commerce platform, our modern digital and mobile applications create predictable, informed, and convenient experiences between customers and their service professionals in the areas of Home \& Field Services, Health Services, and Wellness industries.
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://careers.evercommerce.com/us/en
We are looking for a Automation and AI Specialist \-GTM at EverHealth. EverHealth is simplifying the business of healthcare through simplified, user\-centric software which streamlines the daily operations of healthcare practices. The right software can be critical to the health of a practice, and that’s why we’ve created an integrated ecosystem of intuitive, easy to use products that work with how providers run and grow their business.
Role:
EverHealth is seeking a hands\-on builder, practitioner, and trainer who designs, builds and deploys AI\-enabled solutions that improve efficiency, reduce manual work and expand the capabilities across the GTM teams. This role actively builds copilots, agentic solutions, agents, and automations, and partners directly with the business teams to deliver working solutions. You will assess AI tools and various vendors, implement and optimize automations and solutions ensuring optimal usage, recommend tools to be retired.
This role sits at the intersection of revenue operations, GTM systems, and applied AI. Your focus is execution: designing, building, and deploying automation solutions that deliver measurable operational improvements.
In addition to delivery, this role plays a critical enablement function in upskilling teams, establishing repeatable patterns, and driving adoption of AI tools while ensuring all solutions align with enterprise security, privacy, and Responsible AI standards. The guiding philosophy for this role is Simple, Scalable, Secure, and Responsible.
Responsibilities:
- Design, build, test, and deploy AI\-enabled and automation\-driven workflows for GTM processes
- Apply LLM capabilities and AI tooling to improve GTM and RevOps productivity and execution quality
- Create reusable assets such as agent templates, prompt libraries, automation patterns, and reference flows.
- Implement end\-to\-end automations triggered by copilots/agents or business events (approvals, notifications, data updates).
- Use automation and orchestration platforms (e.g., n8n, API integrations, MCP servers) to develop scalable solutions
- Own delivery from prototype through production\-ready solution, including testing, documentation, and handoff.
- Partner with GTM teams to translate business requirements into technical solutions
- Identify technical risks, constraints, and dependencies early in solution design
- Ensure solutions align with Everhealth privacy, security, and governance standards
- Design and deliver hands\-on training for employees, makers, and business teams on AI\-enabled workflows.
- Maintain documentation for deployed workflows and integrations
- Monitor performance and reliability of automation solutions
- Ensure AI solutions include clear usage guidance, limitations, and human oversight expectations.
- Coach teams on prompting best practices, output validation, and appropriate use of AI in daily work. Serve as a go\-to internal expert through workshops, office hours, and direct support.
- Help establish guardrails that enable safe innovation without unnecessary friction.
- Stay current on emerging AI models, automation tools, and workflow orchestration platforms
Skills and Experienceneeded for success in this role:
Required
- Bachelor’s degree in Computer Science, Information Systems, Data Science, Business Analytics, or a related field, or equivalent practical experience.
- 2\+ years delivering automation, low\-code solutions, analytics, or AI\-enabled capabilities in an enterprise or GTM environment.
- Working knowledge of AI models, LLM applications, prompt orchestration, and AI\-driven workflow design
- Hands\-on experience building automation workflows or API integrations
- Ability to translate business requirements into practical technical solutions
- Experience working cross\-functionally with engineering, data, and business teams
Preferred
- Experience with automation and orchestration platforms (e.g., n8n, Zapier, custom API integrations, MCP servers)
- Experience using Salesforce and Marketo or Hubspot
- Healthcare or health\-tech industry background a plus
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 or Canada – 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, and this role may require travel to our Corporate Headquarters in Denver, Colorado, or to other office locations around North America.
Benefits and Perks (JUST U.S.):
- Flexibility to work where/how you want within your country of employment – in\-office, remote, or hybrid
- Robust health and wellness benefits, including an annual wellness stipend
- 401k with up to a 4% match and immediate vesting
- Flexible and generous (FTO) time\-off
- Employee Stock Purchase Program
- Student Loan Repayment Program
Compensation: The target base compensation for this position is $90,000\- $100,000 per year in most US locations. 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!
Salary Context
This $90K-$100K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($95K) sits 57% below the category median. Disclosed range: $90K to $100K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Evercommerce AI Hiring
Evercommerce has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $100K - $100K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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