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About This Role
The Company:
Serving the People Who Serve the People
Granicus is driven by the excitement of building, implementing, and maintaining technology that is transforming the Govtech industry by bringing governments and its constituents together. We are on a mission to support our customers with meeting the needs of their communities and implementing our technology in ways that are equitable and inclusive. Granicus has consistently appeared on the GovTech 100 list over the past 5 years and has been recognized as the best companies to work on BuiltIn.
Over the last 25 years, we have served 5,500 federal, state, and local government agencies and more than 300 million citizen subscribers power an unmatched Subscriber Network that use our digital solutions to make the world a better place. With comprehensive cloud\-based solutions for communications, government website design, meeting and agenda management software, records management, and digital services, Granicus empowers stronger relationships between government and residents across the U.S., U.K., Australia, New Zealand, and Canada. By simplifying interactions with residents, while disseminating critical information, Granicus brings governments closer to the people they serve—driving meaningful change for communities around the globe.
Want to know more? See more of what we do here.
Job Summary:
The Granicus AI Advisor is a sales overlay team is a dynamic, results\-driven group charged with driving new demand and customer buy\-in and bookings in our Emerging Technology team focused on AI\-native solutions to improve resident experiences and staff efficiency. This is a team that values individual initiative as well as teamwork. What we do makes a difference. Our products and services improve the efficiency of government and the quality of people’s lives. The AI Advisor generates opportunities for our Government Experience Agent solution and related services in an assigned territory and supports early stage discovery and qualification – working across market segments and verticals in partnership with territory Account Managers and AI value engineers.
What Your Impact Will Look Like:
- Generate demand through targeted outreach initiatives in partnership with AI GTM strategy team and demand generation team to prospect ideal customers
- Conduct a high volume of AI discovery calls with public sector prospects and existing Granicus customers
- Run structured discovery to identify the customer’s highest\-value AI and process automation opportunities
- Partner with BDRs and AEs on early stage discovery, as well as AI Value Engineers on discovery and solutioning phases
- Own opportunity progression from initial conversation through qualified opportunity; partner with AI Process Analysts on continued opportunity progression
- Scope and propose pre\-sales engagements, including workshops, on\-site working sessions, demonstrations, and forward\-deployed team pilots
- Maintain disciplined pipeline hygiene in Salesforce, including meeting outcomes, qualification notes, next steps, and accurate stage progression
- Partner with RevOps and Marketing on lead routing, qualification criteria, and feedback loops to improve top\-of\-funnel quality
- Surface strategic customer patterns such as use cases, objections, and readiness gaps to Product, Product Marketing, and Marketing teams to help sharpen Granicus’ AI solution and GTM strategy
- Collaborate with Account Executives across segments to identify Granicus AI opportunities inside existing customers, as well as prospects, and serve as the AI expert on client\-facing calls
You Will Love This Job If You Have:
- 5\+ years in a customer\-facing pre\-sales, solution consulting, customer success, or discovery\-focused role at a software company
- Demonstrated expertise running a high volume / cadence of customer meetings – this role is built for someone who wants to be in front of customers and having conversations
- Fluent in AI, automation, and agentic concepts; you can hold an informed conversation about where AI fits in a customer’s workflow / from a business perspective without needing to be a deep technologist
- Strong discovery skills: you ask sharp questions, listen for signal, and translate customer language into business problems and use cases
- Experience operating in a matrixed go\-to\-market model alongside AEs, BDRs, marketing, and technical specialists
- Public sector or govtech experience: familiarity with state and local government pain points, procurement, and budget cycles is a plus
- Proven track record of generating qualified opportunities and partnering with sales – you are measured on a combination of \[meetings conducted, opportunities qualified, engagements scoped, and AI bookings]
- Excellent written and verbal communication skills; comfortable presenting to director and executive\-level government stakeholders
- Salesforce proficiency and disciplined CRM hygiene
Pay Range: USD $90,000\.00 \- USD $100,000\.00 /Yr. About Us:
Don’t have all the skills/experience mentioned above? At Granicus, we are trying to build diverse, inclusive teams. We do not have degree requirements for most of our roles. If you don’t meet every requirement above but are excited to learn more, we encourage you to apply. We might just be able to find another role that could be a perfect fit! Security and Privacy Requirements* Responsible for Granicus information security by appropriately preserving the Confidentiality, Integrity, and Availability (CIA) of Granicus information assets in accordance with the company's information security program.
- Responsible for ensuring the data privacy of our employees and customers, their data, as well as taking all required privacy training in a timely manner, in accordance with company policies.
The Team* We are a remote\-first company with a globally distributed workforce across the United States, Canada, United Kingdom, India, Armenia, Australia, and New Zealand.
The Culture* At Granicus, we are building a transparent, inclusive, and safe space for everyone who wants to be
a part of our journey.
- A few culture highlights include – Employee Resource Groups to encourage diverse voices
- Coffee with Mark sessions – Our employees get to interact with our CEO on very important and
sometimes difficult issues ranging from mental health to work\-life balance and current affairs.
- Microsoft Teams communities focused on wellness, art, furbabies, family, parenting, and more.
- We bring in special guests from time to time to discuss issues that impact our employee
population
The Impact* We are proud to serve dynamic organizations around the globe that use our digital solutions to make the world a better place — quite literally. We have so many powerful success stories that illustrate how our solutions are impacting the world. See more of our impact here.
The Benefits: At Granicus, we offer a comprehensive and flexible benefits package designed to support your well\-being, growth, and work\-life balance—starting from day one.
Here’s what you can expect as a U.S.\-based team member:
Flexibility \& Balance
- Flexible Time Off – Take the time you need to rest, recharge, and live your life.
- Company\-Wide Wellbeing Days – Paid days off to unplug and focus on your mental health.
- Work From Home Reimbursement – Support a productive home office environment.
Health \& Wellness
- Multiple Health Plan Options – Including a 100% employer\-paid plan.
- Employer HSA Contributions – When enrolled in a High\-Deductible Health Plan.
- Fitness Reimbursement Program – Stay active, your way.
- On\-Demand Mental Health Support – Access to Headspace and other wellness tools.
Family \& Future
- Paid Parental Leave – For both birthing and non\-birthing parents.
- Traditional \& Roth 401(k) – With a generous company match.
- Life \& AD\&D Insurance – 100% employer\-paid coverage for peace of mind.
Growth \& Recognition
- Online Learning Platforms – Fuel your professional development.
- Competitive Salary \& Bonuses – Your contributions are valued and rewarded.
Equal Opportunity Employer: Granicus is committed to providing equal employment opportunities. All qualified applicants and employees will be considered for employment and advancement without regard to race, color, religion, creed, national origin, ancestry, sex, gender, gender identity, gender expression, physical or mental disability, age, genetic information, sexual or affectional orientation, marital status, status with regard to public assistance, familial status, military or veteran status or any other status protected by applicable law.
Salary Context
This $90K-$100K range is in the lower quartile 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 CentraState Healthcare System, 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 ($95K) sits 47% below the category median. Disclosed range: $90K to $100K.
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.
CentraState Healthcare System AI Hiring
CentraState Healthcare System has 6 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $100K - $250K.
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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