Interested in this AI/ML Engineer role at Spatial Data Logic?
Apply Now →Skills & Technologies
About This Role
### About GovPilot:
GovPilot has spent over 20 years building the technology that local governments run on. Today, hundreds of local governments and environmental health regulatory agencies across North America rely on our suite of software products to automate workflows, digitize records, and streamline permitting and compliance — helping public agencies serve their communities better.
We're an AI\-first company. Not in how we talk about AI, but in how we expect people to use it. Our CEO, Javier, sums it up simply: your job isn't just about finding ways AI can help you do the job you have today, it's about rethinking how AI changes the job itself. This is a process that you can drive, not only for greater efficiency but also for greater enjoyment. We see a future where AI can make our jobs not just faster but also more enjoyable and rewarding. As a result this is a huge opportunity, not a threat: AI takes on the repetitive, so people can focus on judgment, creativity, and the problems that actually need a person.
We're building the team that figures out what AI\-driven software delivery looks like at GovPilot, not just using AI tools, but rethinking how software gets built, shipped, and run.
### What We Offer:
- Competitive salary
- Unlimited PTO
- Flexible hours and remote work
- Comprehensive health, dental, and vision insurance
- Professional development budget
- Home office and IT budget
- Real autonomy and high\-impact work
*Please note: we're unable to sponsor or take over sponsorship of an employment visa at this time.*
### The Role:
We're looking for an AI engineer who wants to help us rewrite the rules of how we build software. You'll work across the GovPilot ecosystem, GovTech, SDL, and Hedgerow, on a team figuring out what comes after the standard agile playbook when AI agents can write, test, and ship code.
This is not a role for someone who wants a defined stack and a backlog. You'll move across languages and codebases as needed. You'll be judged on outcomes, not on hours logged or tickets closed. We want someone who runs fast, sets up their own systems, and pushes AI\-assisted development further than we've taken it so far, and does it safely.
You should be someone who is obsessed with AI engineering: you read the papers, try the new tools the week they ship, and have opinions about where this is all going.
### What You'll Do:
- Build and ship features across the GovPilot, SDL, and Hedgerow codebases, picking up new languages and frameworks as the work demands
- Design and build systems and workflows that let AI agents work at scale without sacrificing safety or quality
- Run your own email, calendar, and task management with a high degree of autonomy. No one is going to hand you a step\-by\-step plan
- Propose and pilot new ways of working that go beyond standard agile or sprint\-based development
- Set up guardrails, evals, and review processes so AI\-generated work can be trusted and shipped quickly
- Partner with engineering leadership to figure out what our development lifecycle should look like a year from now, and start building toward it now
What You Bring:
- Deep hands\-on experience with AI coding tools (Claude Code, Cursor, Copilot, or similar) as a daily driver, not an occasional assist
- Comfort moving across programming languages and stacks without needing ramp\-up time
- A track record of working with minimal oversight: you set your own priorities and follow through
- Strong instincts for what can and can't be trusted to run autonomously, and the judgment to build guardrails around it
- Genuine curiosity about how AI changes software development, not just as a productivity boost but as a different way of working
- You must reside in and be authorized to work in the United States
### What Makes You Stand Out:
- Experience designing or running agentic workflows in production, not just prototypes
- A history of building or proposing new team processes, not just following the ones you were given
- Experience across both legacy and modern systems
- A public track record (blog, open source, talks) of thinking seriously about AI engineering
How to Apply
----------------
If you're the kind of person who's already been experimenting with agentic AI workflows on your own time, and you want to help a team figure this out for real, send us your resume.
Equal Opportunity Employer
------------------------------
We are an equal opportunity employer and value diversity. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We believe our workforce should reflect people of all backgrounds, identities, and experiences.
Join us in creating amazing technology solutions that make a difference. Apply today!
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 Spatial Data Logic, 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.
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.
Spatial Data Logic AI Hiring
Spatial Data Logic 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 $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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.