Interested in this AI Software Engineer role at Granicus?
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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: Granicus serves more than 7,000 public-sector agencies and powers approximately 30 billion digital interactions annually. Within the Office of the CTO, we operate an AI-native software development lifecycle: a production engineering model in which autonomous agents perform high-confidence implementation work and senior engineers orchestrate, review, and own the outcome. The model is established and operating; we are scaling it across additional delivery teams.
This role joins one of those teams. Our engineering standards are calibrated to the practices used by leading software and AI organizations — generator-verifier architecture, eval-driven development, staged deployment, and high-volume autonomous pull-request pipelines — operated within a FedRAMP-authorized environment. We are hiring practitioners who will set and uphold these standards, not engineers who simply use AI tooling.
Operating constraints (non-negotiable):agents execute only within branches; all agent-generated code passes senior human review before merging to production; autonomous execution is a force multiplier and does not transfer accountability away from the responsible engineer. Compliance obligations — NIST 800-53 Rev 5, WCAG, SOC 2, and applicable FedRAMP authorizations — are treated as engineering requirements and a source of competitive advantage.
Why this role exists
When agents produce the majority of initial implementation, the scarce engineering skill shifts from authoring code to directing it, reviewing it rigorously at volume, and owning whether it is correct. This role is for a senior engineer who has made that transition: decomposing work for the agent array, authoring the evaluation suites that hold agent output to a measurable standard, reviewing agent-generated pull requests with greater rigor than most engineers apply to their own work, and shipping production code within a FedRAMP-authorized environment. The role contributes to defining how engineers operate in an AI-native lifecycle, not merely to adopting AI tooling.
What Your Impact Will Look Like:
- Direct the agent array on production workstreams — decompose problems into tasks suitable for agent execution, dispatch them, and integrate the output into shipped software.
- Review agent-generated pull requests at volume and at depth — identify correctness, security, and accessibility defects that automated tests do not catch, while maintaining review throughput and a consistent quality bar.
- Author evaluation suites that make quality measurable — define criteria under which the pipeline validates correctness rather than relying on subjective assessment. Eval-driven development is your standard practice.
- Own quality end to end — correctness, performance, security posture, and WCAG accessibility of the software your team ships, irrespective of which component or agent produced the initial implementation.
- Advance workstreams along the autonomy ladderon the basis ofevidence — move work from supervised to autonomous execution when measured reliability supports it, and revert promptly when it does not.
- Strengthen the development lifecycle itself — identify where patterns, prompts, or pipeline components degrade at volume and work with the lead architect to remediate them.
- Maintain agent operations within the security boundary — branch-only execution, vaulted credentials, sandboxed actions, and in-VPC inference. Throughput does not justify exceptions.
- OWNERSHIP FROM DAY ONE + An active workstream directed through the agent array + Agent pull-request review as a core, high-signal responsibility + Evaluation suites authored and owned for your team’s deliverables + End-to-end quality ownership within the FedRAMP-authorized environment
- SCOPE YOU WILL GROW INTO + Higher autonomy ratios substantiated by reliability data + Reusable prompt and evaluation patterns adopted by other engineers + Contribution to the lifecycle standards as a co-author, not only a user + Mentorship of engineers transitioning to agent-directed delivery
You Will Love This Job If You Have: Required
- Strong engineering fundamentals. Data structures, systems design, and testing, with the ability to read unfamiliar code quickly and assess it accurately. Agents amplify engineering judgment; they do not substitute for it.
- A record of shipping production software youownedthe quality of, including responsibility for diagnosis and remediation when it failed.
- Hands-on experience directing coding agents on production work, including their failure modes and the practice of reviewing agent-generated code critically rather than approving it by default.
- Demonstrated code-review competence. You identify defects that automated tests do not catch, provide actionable feedback, and maintain a high bar without becoming a bottleneck.
- High autonomy. You advance work without step-by-step direction and escalate issues proactively.
Preferred
- High-assurance or regulated experience. Shipping within FedRAMP, defense, financial services, healthcare, or another NIST 800-53 / SOC 2 / HIPAA-bound environment.
- Depth inevaluationauthoring or test-first development within a rigorous engineering culture.
- Full-stack range, sufficient to review front-end and back-end agent output with equal confidence.
- Public-sector orgovtechexperience and familiarity with the relevant end users.
Indicators of a strong fit
- You deliver more effectively by directing multiple agentsthan by authoring code directly, while retaining a precise understanding of the intended implementation.
- You regard rigorous code review as a core engineering competency.
- You prefer toestablishquality through evaluation suitesrather than through discussion.
- You treat the agent array as leverageand reliably identify when its output is incorrect.
- You want to contribute to how engineering is practiced here,not only to apply new tooling.
Indicators this role is not a fit
- You measure your contribution by lines of code personally authored.That metric does not apply in this model.
- You approve pull requests without thorough review under time pressure.At agent volume, this is the primary path by which defects reach production.
- You are uncomfortable owningquality forcode you did not author.Ownership here is intentionally decoupled from authorship.
- You regard evaluation and compliance as impediments to shipping.
- You prefer narrowly scoped ticketsand no involvement in how the system operates.
Pay Range: USD $88,000.00 - USD $180,000.00 /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 $88K-$180K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $192K across 146 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 8,943 AI roles we're tracking, AI Software Engineer positions make up 4% of the market. At Granicus, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills in Demand for This Role
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($134K) sits 39% below the category median. Disclosed range: $88K to $180K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Granicus AI Hiring
Granicus has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US. Compensation range: $180K - $180K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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