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About This Role
Salary
Depends on Qualifications
LocationOrange County, CA
Job Type
Full\-Time
Job Number
00913
Department
Revenue Ops, Training \& Enablement
Division
General
Opening Date
06/03/2026
About
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Please note this will be an onsite role Mon\- Fri in San Clemente, CA. or Los Angeles, CA. Join the Future of GovTech with the SaaS Team Transforming How Communities Work
*At NEOGOV, we’re hiring mission\-driven innovators who want to grow their careers, drive transformative technology, and make a meaningful impact on the communities our customers serve.*
For over 25 years, NEOGOV has been at the forefront of public\-sector innovation, building technology that helps government and education organizations work smarter, move faster, and better serve their communities.
We are a team of cutting\-edge technologists, operators, and problem\-solvers with top\-tier skills and a shared ambition: to build the most efficient operating model in GovTech. We combine deep public\-sector expertise with modern SaaS practices, AI\-enabled workflows, and a relentless focus on execution.
At our core, we are a people\-first company. We empower our customers to support the public servants they employ, the communities they serve, and the talented teams that bring our platform to life.
Our solutions support the entire employee journey, from hire to retire. By transforming complex HR and compliance workflows into simple, intuitive experiences, we help public\-sector organizations reduce friction, improve efficiency, and focus on what matters most: making a difference.
NEOGOV is seeking a highly adaptable Revenue AI \& Automation Architect to bridge the gap between rich sales data and real\-time commercial execution through transformative AI development and implementation. Grounded in strong analytical judgment, this is a strategic systems\-building role focused on designing the end\-to\-end AI pipelines, workflow architectures, and prompt engines that transform raw data into actionable intelligence. Rather than simply generating static reports, you will build a scalable engine that delivers role\-specific insights directly to sales and marketing leadership—measuring success not by outputs, but by changed behaviors and direct pipeline impact. As a forward\-thinking builder with a high learning velocity, you will own this revenue intelligence loop end\-to\-end, with the long\-term vision to scale across the organization. *This posting is for an existing vacancy.*
What You Will do
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- Own the end\-to\-end system architecture design, data architecture, automation design, and maintenance of NEOGOV’s AI\-powered Revenue Operating System
- Manage and operate the integration layer connecting sales and marketing technologies — ensuring data flows cleanly, fields are mapped correctly, and the system is production\-reliable every month.
- Build and continuously improve the AI workspace that transforms structured data inputs into role\-specific intelligence — designing the prompt architecture, output formats, and quality controls for each audience.
- Design and maintain the monthly signal data pack: a structured feed that transforms raw conversation and pipeline data into the AI workspace inputs that drive all downstream outputs.
- Define and maintain the signal taxonomy — what to track (competitor mentions, objection patterns, champion\-loss signals, persona gaps), how to weight each signal, and how to deliver insights to the right audience.
- Proactively identify and remediate data quality gaps; work with Gong, Salesforce, and Pursuit admins to resolve field mapping issues, missing data, and pipeline discrepancies before they affect intelligence outputs.
- Design and deploy feedback capture frameworks to ensure frontline customer conversations across all departments feed back into the central AI system.
- Engineer and maintain distinct intelligence outputs for each stakeholder tier: CRO brief and operating pack, Director scorecard, seller action sheet, and Marketing Intelligence brief — each calibrated for depth, format, and decision support.
- Evaluate, test, and integrate emerging AI tools and platforms as the landscape evolves; maintain a current view of what tooling best serves each workflow use case and advise leadership on adoption decisions.
- Translate complex data patterns into executive\-grade strategic narrative — framing 'what is the market telling us' and 'what should we do next' in terms that drive decisions at the CRO and C\-suite level.
- Proactively identify data gaps and partner with sales tools platform administrators to resolve technical discrepancies.
Who You Are
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- 3–7 years of experience in Revenue Operations, Sales Strategy, Commercial Analytics, or a directly adjacent function within B2B SaaS environment
- Demonstrated hands\-on experience designing and operating AI or automation workflows — not simply using AI tools, but architecting the systems around them, including prompt design, structured outputs, and multi\-step pipelines.
- Salesforce CRM proficiency at a working\-owner level: report building, pipeline hygiene, custom object navigation, and workflow familiarity — comfortable owning your data layer, not just consuming it.
- Conversation intelligence platform experience (Gong preferred) at a power\-user or admin level: scorecards, call trackers, category frameworks, and trend analysis.
- Proven ability to translate data into executive\-grade insight and strategic narrative
- Functional comfort with data pipeline concepts and structured data formats (CSV, JSON); SQL or Python proficiency is a strong differentiator, but an analytical mindset and ability to learn technical tooling matters more than a specific language.
- Experience with or demonstrated understanding of MCP (Model Context Protocol), API integrations, or equivalent data connector frameworks — comfort building across connected systems rather than within a single tool.
- Strong written and verbal communication skills at an executive level
- Familiarity with ABM (Account\-Based Marketing) strategy and the role of intent data, persona mapping, and share\-of\-voice measurement in pipeline development.
- Prior experience with third\-party intelligence platforms such as Pursuit.us, Starbridge, 6Sense, or comparable tools
- Bachelor's degree in Business, Analytics, Marketing, Information Systems, or a related field; advanced degree or professional certifications in data, AI, or revenue operations are a plus, but not required.
Essential Functions:* Work in NEOGOV’s San Clemente, CA. or El Segundo, CA. office
- Ability to keep up with continuous learning through self\-education
- Sit or stand for prolonged periods of time
What NEOGOV Offers
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- Competitive Wages
- Comprehensive Benefits package (medical, dental, vision, etc.) for full\-time employees effective Day 1
- Generous PTO to support work\-life balance
- 401K Matching
- 12\-week Paid Parental Leave
- Autonomy to grow and find your career path with supportive leadership
- Inclusive and diverse work environment
*NEOGOV does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, protected military status, or other non\-merit factors.*
Our hiring process may include Artificial Intelligence (AI) screening for keywords and minimum qualifications. Recruiters review all results.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At NEOGOV, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
NEOGOV AI Hiring
NEOGOV has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span CA, US, Remote, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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